Interpreting the Meaning of Clinical Values in Electronic Medical Records

ABSTRACT

Mechanisms are provided for determining the meaning of medical values in natural language content. The mechanisms receive patient information from a source which includes at least one of text or a numerical value indicative of a medical value corresponding to a patient. The mechanisms perform cognitive natural language processing on a context of the text or numerical value in the patient information and determine a meaning of the medical value based on results of the cognitive natural language processing. The mechanisms then process the patient information from the source based on the determined meaning of the medical value.

BACKGROUND

The present application relates generally to an improved data processingapparatus and method and more specifically to mechanisms forinterpreting the meaning of clinical values in electronic medicalrecords.

Monitoring patients with chronic illnesses, such as congestive heartfailure, diabetes, and asthma represents one of the greatest challengesfacing modern medicine. Patients with chronic illnesses require ongoing,follow-up treatment and care to properly manage their conditions.Unfortunately, a number of these patients do not receive ongoingtreatment and care, receive treatment and care on a sporadic basis, orreceive treatment and care which is not in accordance with recommendedguidelines. Worse, patients often fail to do the basic simple day-to-daytasks that could prevent or reduce the frequency and magnitude of acatastrophic event such as a hospitalization. As a result, thesepatients often unnecessarily suffer from symptoms of their chronicillness which would have been minimized or prevented with proper ongoingtreatment and care. Additionally, some of these patients may laterrequire hospitalization, or in severe cases some of these patients maydie, both of which may have been prevented if the patient was receivingthe proper ongoing treatment and care.

In order to track the treatment of patients, medical practices andmedical service providers have used various forms of record keeping tomaintain a medical history for the patient. While these medical recordsand medical histories were originally maintained in a hard copy formrequiring physical copying and transfer of these records from onemedical practice and/or medical service provider to another, with theadvent of computer technologies, the maintaining of such records andtheir transfer has been greatly improved.

The American Recovery and Reinvestment Act of 2009 government mandatesthat medical practices adopt and demonstrate “meaningful use” ofelectronic health records (EHRs) in order to maintain their existingMedicate and Medicare reimbursement levels. In addition, the Act alsoprovides financial incentives for healthcare providers who prove suchmeaningful use of EHRs. A meaningful use of EHRs is defined by thefederal government of the United States of America as using digitalmedical and health records to improve quality, safety, efficiency, andreduce health disparities, engage patients and family, improve carecoordination and population and public heath, and maintain privacy andsecurity of patient health information. The Act also provides penaltiesfor non-compliance with the Act including a reduction in Medicatereimbursements.

Prior to the Act, many medical practices already implemented computerbased electronic medical records (EMRs) for the inherent time saving andefficiency features of such computer based systems. These computersystems are varied and have various capabilities depending on theparticular implementation of the hardware and software.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described herein in the DetailedDescription. This Summary is not intended to identify key factors oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

In one illustrative embodiment, a method is provided, in a dataprocessing system comprising at least one processor and a memory, fordetermining the meaning of medical values in natural language content.The method comprises receiving patient information from a source. Thepatient information comprises at least one of text or a numerical valueindicative of a medical value corresponding to a patient. The methodfurther comprises performing, by the data processing system, cognitivenatural language processing on a context of the text or numerical valuein the patient information. Moreover, the method comprises determining,by the data processing system, a meaning of the medical value based onresults of the cognitive natural language processing. In addition, themethod comprises processing, by the data processing system, the patientinformation from the source based on the determined meaning of themedical value.

In other illustrative embodiments, a computer program product comprisinga computer useable or readable medium having a computer readable programis provided. The computer readable program, when executed on a computingdevice, causes the computing device to perform various ones of, andcombinations of, the operations outlined above with regard to the methodillustrative embodiment.

In yet another illustrative embodiment, a system/apparatus is provided.The system/apparatus may comprise one or more processors and a memorycoupled to the one or more processors. The memory may compriseinstructions which, when executed by the one or more processors, causethe one or more processors to perform various ones of, and combinationsof, the operations outlined above with regard to the method illustrativeembodiment.

These and other features and advantages of the present invention will bedescribed in, or will become apparent to those of ordinary skill in theart in view of, the following detailed description of the exampleembodiments of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention, as well as a preferred mode of use and further objectivesand advantages thereof, will best be understood by reference to thefollowing detailed description of illustrative embodiments when read inconjunction with the accompanying drawings, wherein:

FIG. 1 is a block diagram illustrating a cloud computing system 100 forproviding software as a service, where a server provides applicationsand stores data for multiple clients in databases according to oneexample embodiment of the invention;

FIG. 2 is another perspective of an illustrative cloud computingenvironment in which aspects of the illustrative embodiments may beimplemented;

FIG. 3 is an example diagram illustrating a set of functionalabstraction layers provided by a cloud computing environment inaccordance with one illustrative embodiment;

FIG. 4 is an example block diagram illustrating the primary operationalelements of such a personalized patient care plan creation andmonitoring system in accordance with one illustrative embodiment;

FIG. 5 is a flowchart outlining an example operation for creating apersonalized patient care plan in accordance with one illustrativeembodiment;

FIG. 6 is a flowchart outlining an example operation for monitoring apatient's performance with regard to a prescribed personalized patientcare plan in accordance with one illustrative embodiment;

FIG. 7 is a flowchart outlining an example operation for adjusting apersonalized patient health care plan based on an evaluation of apatient's adherence to a prescribed personalized patient health careplan in accordance with one illustrative embodiment;

FIG. 8 is an example diagram of an example hierarchical set of rules inaccordance with one illustrative embodiment;

FIGS. 9A-9D are diagrams illustrating example graphical user interfacesfor rule generation in accordance with one illustrative embodiment;

FIG. 9E represents an example rule flow illustrating the application ofa rule in accordance with one illustrative embodiment;

FIG. 10 is an example diagram illustrating a use of a variable listinginput to a clinical rule in accordance with one illustrative embodiment;

FIG. 11 is an example block diagram of primary operational elements foridentifying successful outcome cohorts in accordance with oneillustrative embodiment;

FIG. 12 is a flowchart outlining an example operation for identifyingsuccessful outcome cohorts in accordance with one illustrativeembodiment;

FIG. 13 is a flowchart outlining an example operation for applying anintervention action to a non-compliant patient in accordance withpreviously identified successful outcome sub-cohorts in accordance withone illustrative embodiment;

FIG. 14 is an example block diagram of the primary operational elementsfor selecting an optimum or best communication mode or sequence/patternof communication modes in accordance with one illustrative embodiment;

FIG. 15 is a flowchart outlining an example operation for selecting abest mode or sequence/pattern of communication modes in accordance withone illustrative embodiment;

FIG. 16 is an example block diagram of the primary operational elementsof a clinical value interpretation engine in accordance with oneillustrative embodiment; and

FIG. 17 is a flowchart outlining an example operation for interpretingclinical values in electronic medical records in accordance with oneillustrative embodiment.

DETAILED DESCRIPTION

Before beginning the discussion of the various aspects of theillustrative embodiments, it should first be appreciated that throughoutthis description the term “mechanism” will be used to refer to elementsof the present invention that perform various operations, functions, andthe like. A “mechanism,” as the term is used herein, may be animplementation of the functions or aspects of the illustrativeembodiments in the form of an apparatus, a procedure, or a computerprogram product. In the case of a procedure, the procedure isimplemented by one or more devices, apparatus, computers, dataprocessing systems, or the like. In the case of a computer programproduct, the logic represented by computer code or instructions embodiedin or on the computer program product is executed by one or morehardware devices in order to implement the functionality or perform theoperations associated with the specific “mechanism.” Thus, themechanisms described herein may be implemented as specialized hardware,software executing on general purpose hardware, software instructionsstored on a medium such that the instructions are readily executable byspecialized or general purpose hardware, a procedure or method forexecuting the functions, or a combination of any of the above.

The present description and claims may make use of the terms “a”, “atleast one of”, and “one or more of” with regard to particular featuresand elements of the illustrative embodiments. It should be appreciatedthat these terms and phrases are intended to state that there is atleast one of the particular feature or element present in the particularillustrative embodiment, but that more than one can also be present.That is, these terms/phrases are not intended to limit the descriptionor claims to a single feature/element being present or require that aplurality of such features/elements be present. To the contrary, theseterms/phrases only require at least a single feature/element with thepossibility of a plurality of such features/elements being within thescope of the description and claims.

In the following description, reference is made to embodiments of theinvention. However, it should be understood that the invention is notlimited to specific described embodiments. Instead, any combination ofthe following features and elements, whether related to differentembodiments or not, is contemplated to implement and practice theinvention. Furthermore, although embodiments of the invention mayachieve advantages over other possible solutions and/or over the priorart, whether or not a particular advantage is achieved by a givenembodiment is not limiting of the invention. Thus, the followingaspects, features, embodiments and advantages are merely illustrativeand are not considered elements or limitations of the appended claimsexcept where explicitly recited in a claim(s). Likewise, reference to“the invention” shall not be construed as a generalization of anyinventive subject matter disclosed herein and shall not be considered tobe an element or limitation of the appended claims except whereexplicitly recited in a claim(s).

In addition, it should be appreciated that the present description usesa plurality of various examples for various elements of the illustrativeembodiments to further illustrate example implementations of theillustrative embodiments and to aid in the understanding of themechanisms of the illustrative embodiments. These examples are intendedto be non-limiting and are not exhaustive of the various possibilitiesfor implementing the mechanisms of the illustrative embodiments. It willbe apparent to those of ordinary skill in the art in view of the presentdescription that there are many other alternative implementations forthese various elements that may be utilized in addition to, or inreplacement of, the examples provided herein without departing from thespirit and scope of the present invention.

Overview

As noted above, providing treatment and care for patients having illnessrequiring ongoing treatment is a major issue in modern medicine. Manytimes this ongoing treatment and care is a shared responsibility betweenthe medical workers, e.g., doctors, nurses, etc. and the patient. Thatis, the patient must perform certain actions on their own to provideself-treatment for the illness, which often involves making differentlifestyle choices, e.g., changing diet, increasing physical activity,taking prescribed medications, eliminating habits and consumption ofproducts that are detrimental to health, etc., with the medical workersproviding monitoring and periodic checks of the patient's progress toensure that the patient is adhering to the treatment needed to controland/or improve the patient's condition.

A number of mechanisms have been developed for assisting the patient andmedical workers in handling their shared responsibilities includingmechanisms for generating patient care plans based on the patient'smedical condition, mechanisms for patient's to self-monitor theiradherence to their own care plans, and the like. Such mechanisms oftenregard patients as generic types of patients, e.g., a generic asthmapatient, a generic diabetes patient, etc. possibly with someclassification within these generic categories based on the patient'sage, gender, race, and other generic demographics. Even with suchclassification within the generic categories, the resulting care planassociated with the patient is one that is applicable to multiplepatients having the same set of medical diagnosis and demographics. Thecare plan is not in fact personalized to the specific patient but to ageneral categorization of the patient.

Each individual patient has a specific and different set of lifestyleconditions that make that patient unique from other patients. It is thisuniqueness that is not reflected in the patient care plans generated byknown mechanisms.

That is, the known patient care plan mechanisms are created to classifypatients into generic categories and apply generic care plans to thesepatients. While mechanisms employing such patient care plan mechanismsmay refer to them as being “personalized” or “customized” to thepatient, they in fact are only superficially customized in that they maybe customized based on generic customization categories, e.g.,customized based on generic demographics such as age, race, gender, etc.As a result, patients are not in fact presented with a patient care planthat the patient feels is specifically suited to them. The patient careplans do not in fact take into account the patient's own individualcircumstances and can be applied to a plurality of patients having thesame demographics and medical condition, e.g., all 40 year old femalediabetes patients. There are no mechanisms that personalize a patient'son-going treatment and care based on both their medical condition andthe patient's own personal lifestyle, taking into account multiplelifestyle conditions and the facilities and resources available to thatparticular patient based on their lifestyle.

It should be appreciated that the term “lifestyle” as it is used hereinrefers to the way in which a person lives their lives. The term“lifestyle information” refers to the data collected that characterizesthe lifestyle of the patient and may encompass various temporal,spatial, environmental, and behavioral information/data about thepatient that together comprises a unique combination of information/datathat characterizes and represents the way in which that specific patientconducts their life on a daily basis. The lifestyle information for apatient is specific to that patient and is not generally applicable tomultiple patients. The lifestyle information may be provided at variouslevels of granularity depending upon the particular implementation. Aspart of this lifestyle information, data generated by the specificpatient via one or more computing devices or other data communicationdevices may be included such as actions performed by the patient on adaily basis, personal schedules, specifications of preferences, etc. Forexample, lifestyle information may include the patient enteringinformation, such as into a computing device executing a patienttracking application, indicating that the patient ate breakfast at afast food restaurant in the airport on the way to Virginia this morning.In addition, data generated by external systems associated with thirdparties that characterizes the patient's lifestyle may be included inthe lifestyle information as well, e.g., a healthcare insurance companymay have information about the patient's lifestyle, e.g., smoker,overweight, sedentary, high risk for diabetes, etc., which may becharacteristic of the patient's lifestyle.

For example, with regard to temporal lifestyle information, thelifestyle information may comprise one or more data structuresspecifying one or more schedules of events that the patient undergoeseither on a routine basis or on a dynamic basis, e.g., a baselineroutine schedule that may be dynamically updated as events occur or donot occur. The temporal lifestyle information may comprise, for example,the time that the patient wakes in the morning, when they have theirmeals, when they go to work and return home, when they take theirchildren to school, when they shop for groceries, when they go to bed atnight, scheduled non-routine events, free time, scheduled flight, ferry,train, or other ground transportation departure/arrival times, and/orany other temporal information characteristic of the patient's dailylife and other non-routine scheduled events.

With regard to spatial lifestyle information, this information maycomprise one or more data structures identifying locations associatedwith the patient's daily lifestyle including routine locationsfrequented by the patient, e.g., the location of their home, thelocation of their work, the location of their child's school, thelocation of the retail establishments that they frequent, the locationof their doctors, the typical travel paths between locations utilized bythe patient, and the like. The spatial lifestyle information may furthercomprise information about each location including the number of storiesor levels in the buildings, e.g., two-story home, five-story officebuilding, etc., whether the location has stairs, etc. The spatiallifestyle information may further comprise geographic informationincluding the city, state, county, country, etc., in which the patientlives, works, travels to, or otherwise conducts their life.

With regard to environmental lifestyle information, this informationcomprises one or more data structures with indications of theenvironmental quality and resource availability in the environments inwhich the patient is present, is predicted to be present at a later time(such as based on the temporal and spatial lifestyle information), ortypically is present on a daily or routine basis. For example,environmental lifestyle information may include information about thepatient's home location, e.g., in a rural, urban, or suburbanenvironment, has access to parks, walking trails, etc. Thisenvironmental lifestyle information may include information about thepatient's work location including whether the patient works in an officesetting with fluorescent lights and relative quiet, in a manufacturingsetting with heavy machinery and loud noises, works with computers themajority of the day, has his/her own office or is in a cubicle, thenumber of co-workers the patient has that they interface with on a dailybasis, the types and/or identities of establishments around thepatient's home/work for purposes of determining access to resources(e.g., products and services), air quality, weather conditions,elevation (for purposes of oxygen level determination, for example), andthe like.

Regarding behavioral lifestyle information, this information comprisesone or more data structures having indications of the patient's ownbehavior and likes/dislikes, i.e. lifestyle preferences. The behaviorallifestyle information may comprise such information as the patient'shabits, responses to communications of different modalities, patterns ofactivity, and the like. For example, such behavioral lifestyleinformation may indicate that the patient has a habit of eating a snackevery evening after 9 p.m. or takes his/her dog for a walk in themornings before 9 a.m. and after 5 p.m. The behavioral lifestyleinformation may further indicate the patient's likes and dislikes(preferences) with regard to various elements of daily life includingtypes of foods the patient likes/dislikes, types of physical activitythe patient likes/dislikes, when the patient likes to engage in certainactivities, e.g., exercising before work/after work, or the like.

The various lifestyle information data may be obtained directly from thepatient, such as via an electronic questionnaire, through analysis ofelectronic medical records (EMRs) or other entries in databasesassociated with the patient (e.g., governmental databases associatedwith a patient's social security number, address, or the like), orotherwise obtained from one or more monitoring devices and/orapplications utilized on one or more computing devices associated withthe patient and with which the patient interacts, e.g., patient trackingapplications on a smart phone, a medical monitoring device, or the like,that monitors physical activity, food logs, and the like. This lifestyleinformation may be generated from static information and may also bedynamically updated on a periodic or constant basis to obtain the mostcurrent lifestyle information representative of the patient's currentlifestyle. The lifestyle information is utilized to customize orpersonalize a patient care plan for the specific patient such that thepatient is presented with a resulting patient care plan that the patientfeels is tailored specifically to them and they way they conduct theirlives.

In addition to known patient care plan mechanism suffering from thedrawback of not in fact generating personalized patient care planstaking into account a patient's unique lifestyle, the known patient careplan mechanisms also do not provide for the ability to integratethird-party information about the lifestyle of a patient into thepatient care plan personalization such that a more completeunderstanding of the capabilities of the patient based on theirlifestyle is realized when generating and monitoring the patient'sadherence to the patient care plan. For example, third-party lifestyleinformation may comprise information from commercial and governmentalcomputing systems, databases, and the like, that characterize thepatient's environment, availability to resources (e.g.,products/services/facilities), etc., or is otherwise ancillary andfurther defining of other lifestyle information associated with thepatient.

As one example, a third-party lifestyle information source may comprisea global positioning system (GPS) source that identifies the patient'sassociated locations, e.g., home, work, etc., and identifiesestablishments around those locations that provide resources that are ofinterest to the patient's lifestyle and potentially of interest ingenerating a patient care plan. For example, specialty grocery stores,vitamin stores, pharmacies, restaurants, gyms, walking paths, parks,recreational areas, community pools, and the like, may be identifiedbased on a GPS system and its associated databases of information. Thisinformation may include identifications of types (e.g., VietnameseRestaurant) and specific identities of the particular establishmentswhich can be used with other third-party lifestyle information sources(e.g., a particular restaurant's website comprising menu and nutritioninformation) to retrieve specific information about those identifiedestablishments. For example, a particular restaurant may be determinedto be within a specified distance of the patient's home location andcorresponding restaurant menu item information and hours of operationinformation may be retrieved from that particular restaurant's website,computing system, or other database. The retrieved menu item informationand hours of operation information may be used, as described hereafter,to correlate the information with patient care plan information, e.g.,nutritional and caloric information may be correlated with the patientcare plan, to generate patient care plan actions/tasks and/orrecommendations for assisting the patient in adhering to the patient'spersonalized patient care plan. Similarly, other third-party lifestyleinformation sources may provide information for correlation with patientcare plan actions/tasks including hours of operations, products/servicesprovided, distance from the patient's locations, and the like.

The illustrative embodiments of the present invention collect patientdemographic and medical data, such as from questionnaires, electronicmedical records, and the like, and generate a baseline patient care planbased on an initial diagnosis of the patient's medical condition, one ormore categorizations of the patient based on the collected demographicand medical data, established patient care plan guidelines, and goals tobe achieved by the patient care plan. Thus, for example, a patient'sdemographic information and electronic medical records may indicate thatthe patient is a 40 year old female that has been diagnosed withdiabetes. Various pre-established categories and sub-categories may bedefined for different types of patients in an ontology based on thevarious demographic and medical history characteristics, e.g., acategory for diabetes patients, a sub-category of patients in the agerange of 40 to 50 years old, a sub-sub-category of female patients, andso on.

Similarly, treatment guidelines may be established for defining ways inwhich to treat various medical maladies with these treatment guidelineshaving various triggering patient characteristics. For example, atreatment guideline may specify that for female diabetes patients thatare in the age range of 40 to 60 years old, the patient should follow alow sugar diet and have at least 30 minutes of stressful exercise perday. A database of such treatments and their guidelines may be providedthat correlates various combinations of patient characteristics with acorresponding treatment. Thus, by categorizing the patient in accordancewith their characteristic information as obtained from demographic andmedical data for the patient, these categories may be used to evaluatethe applicability of the various treatments by matching the categorieswith the patient characteristics of the treatments to identify the besttreatment for the patient, i.e. the treatment having the most matchesbetween the patient categories and the treatment's required patientcharacteristics.

At this point, a general patient care plan is generated for the patientthat identifies the treatment, which may be an on-going treatment, whichshould be prescribed for the patient. A patient care plan in thiscontext is essentially a set of goals and actions for achieving thosegoals. As will be described hereafter, in addition, the presentinvention includes, in a patient care plan, a patient monitoring planwith specific actions to be taken on the part of an assessor to monitorand interface with the patient to elicit positive results from thepatient, e.g., adherence to the patient care plan.

While a general patient care plan is present at this point, the generalpatient care plan has not yet been personalized or customized to thespecific patient's unique lifestyle information. That is, while ingeneral a 40 year old female diabetes patient should follow a low sugardiet with 30 minutes of stressful exercise each day, not every patient'slifestyle will accommodate such actions in the same way.

The illustrative embodiments further operate to personalize the generalpatient care plan to the particular lifestyle of the specific patient.Lifestyle information data is obtained from various sources to obtain anoverall representation of the lifestyle of the patient. Examples of suchsources include geospatial information sources, weather informationsources, commercial establishment websites or computingdevices/databases, governmental or regulatory organization informationsources, and the like.

A patient's lifestyle information may also include data gathered fromsocial media sources, including social media posts, comments, likes,browsing and other activity by the patient to determine their socialcircle, hobbies, likes, dislikes, interests, etc. This may include, butis not limited to, data from websites like Facebook™, Twitter™,Instagram™, Reddit™, Pinterest™, blog posts, and the like. Purchases andshopping activity are also a powerful indicators of lifestyle. Purchasedata may include not only data about past purchases, but also shoppingand activity online. Web browsing and search history similar to thatused in driving online advertising can also be used to build lifestyleinformation and a lifestyle profile for a patient. Membership incustomer loyalty programs from retail stores, grocery chains, andrestaurants can also be used. Data that can be obtained from theseprograms may include membership, frequency of store visits, priorpurchases, and the like. This data provides meaningful information aboutstore, dining, grocery preferences, personal habits and schedules, anddietary data, among other information. This data may be used whenbuilding lifestyle information for the patient using products, goods,services, stores, and restaurants that the patient favors.

These third-party lifestyle information sources may provide lifestyleinformation that is combined with lifestyle information provided by thepatient himself/herself for analysis to identify the types ofpersonalized care plan actions to be used with the patient's care plan,the timing of the actions, and the types and timing of patient care planmonitoring and management actions to be performed by an assessor, e.g.,a human assessor, automated assessment system, or a combination of humanand automated assessment mechanisms. Thus, the selection of patient careplan actions (i.e. patient actions and monitoring actions) is based onthe general patient care plan goals, the general patient care planactions to be performed, and the personalization of these generalpatient care plan actions to the specific lifestyle of the patient.

Various lifestyle information analysis logic is provided to evaluate andclassify the patient's lifestyle in accordance with a number of definedlifestyle categories. For example, the patient's lifestyle may becategorized according to level of physical activity, level ofavailability to healthy food sources, quality of home and workenvironment (lighting, air quality, quietness, safety, etc.), level ofaccess to exercise facilities, various qualitative aspects of thepatient's home and work life, and the like. From these categories, amore specific patient care plan is generated to achieve the goals andactions of the generic patient care plan, e.g., prescribe a specifictype of diet plan which the patient has access to foods that meet withthe diet plan and has a schedule that facilitates preparation ofparticular types of food.

For example, if the patient has limited time due to long work hours,having young children that require attention in the mornings/eveningsbefore/after work, and the like, then food preparation time will bedetermined to be a minimum and thus, a corresponding diet plan will beselected for this particular type of lifestyle involving more processedfoods than another patient that may have more time to perform morecomplex food preparation actions. Similarly, based on the patient'slifestyle information as obtained from the various sources, themechanisms of the illustrative embodiments may prescribe a walkingregimen based on the fact that the patient lives near a walking trail(as obtained from GPS data) and works in a building that has multiplefloors (as obtained from patient supplied lifestyle information, GPSdata, and/or governmental real estate databases) such that walking thestairs is an option. The patient's lifestyle information may furtherindicate an ability to prescribe a strength-building regimen since thepatient lives near a gym (obtained from GPS data) or has gym facilitiesat their office (obtained from the patient supplied lifestyleinformation and/or real estate database information listing amenities ofthe building where the patient works). The timing of such actions may bespecified in the patient care plan such that the walking regimen mayinstruct the patient to take a 25 minute walk at 8 a.m. every weekdayand walk up/down the stairs at their office on their way to and fromwork and to and from lunch. The patient care plan may further specifythat the patient is to go to the gym on Tuesday and Thursday at 7:30p.m. to do 30 minutes of strength building exercise.

The granularity of the patient care plan may be even more specificdepending upon the implementation. For example, with regard to a walkingregimen, a particular path for the patient to walk may be specified inorder to achieve a desired level of stress on the patient may bespecified based on the geospatial information for the patient's home,work, and other locations, e.g., “Walk up Main Street to 2^(nd) Street,take a left, walk along 2^(nd) Street to Picard Street, take a left,walk down Picard Street to 1^(st) Street, take a left, and return tobuilding.” Such a path determination may be made based on informationobtained about the geographical location of the patient's officebuilding including the elevations of the streets to indicate uphill ordownhill walking, distances, etc.

Because the lifestyle information may comprise specific establishmentinformation, the patient care plan actions may be further personalizedto the patient's particular locations and may specify particularestablishments that can be frequented as well as what products/servicesthe patient can utilize to be in compliance with the patient'sprescribed care plan. For example, the menu items at a local restaurantmay be analyzed to identify which menu items meet the diet requirementsof the patient's care plan, e.g., low sugar foods, and the restaurantand its compliant menu items may be provided to the patient as part oftheir patient care plan. Personal trainer information for gyms may beobtained which includes the personal trainers' schedules, classschedules, and times of availability such that the patient may beinstructed, as part of their personal patient care plan, when would bethe best time for them to go to the gym to obtain personal trainerassistance with their strength building exercise regimen.

This more personalized patient care plan may further be customized tothe specific lifestyle of the patient by evaluating the temporallifestyle information and behavioral lifestyle information for thepatient. Thus, having established a set of goals and actions to achievethose goals that are specific to the patient based on theirdemographics, medical data, and the patient's lifestyle information, thegoals and actions may be converted to specific actions to be taken bythe patient on a daily basis. For example, the patient's lifestyleinformation may be further analyzed to identify specific exerciseactions to be taken by the patient based on their location, thefacilities available, the patient's personal schedule of activitiesduring the day, the patient's personal likes/dislikes (preferences),etc. For example, the patient may have a schedule that shows that thepatient is available to exercise between 8 and 9 a.m. and 7:00 p.m. till8:00 p.m. on most weekdays, is not available Thursday evenings afterwork for exercise, is available between 1 and 2 p.m. on Saturdays, andall day on Sundays. The preferences may further state that the patientdoes not like hot or rainy weather. The patient lifestyle informationmay further indicate that the patient likes to sleep late on Saturdaysand Sundays and thus, while available early on these days, themechanisms of the illustrative embodiments may adjust the scheduling ofactions in the personalized care plan to accommodate this timingpreference of the patient. Furthermore, the patient care plan may bedynamically adjusted based on determine weather and temperatureconditions, e.g., instead of a standard walking regime that may havebeen previously part of the patient care plan, because the weatheroutside indicates a temperature of approximately 90 degrees and 20%chance of rain, the patient care plan may be adjusted to walking for 25minutes in a neighborhood shopping mall.

It can be appreciated that because the lifestyle information that may beutilized to provide personalization of patient care plans is varied andvast, the types of personalizations that may be made to a patient careplan are likewise varied and vast. The patient care plan personalizationmechanism of the illustrative embodiments provides logic for analyzingand evaluating a large set of lifestyle information data from varioussources, determine specific patient care plan actions that meet thecategorization and characterization of the patient's lifestyle asobtained from the analysis of the patient's lifestyle information, aswell as achieves the goals and general actions associated with thegeneralized patient care plan corresponding to the patient'sdemographics and medical data, and compose the various personalizedpatient care plan actions into a series of actions to be taken by thepatient over a set time period, e.g., daily, weekly, monthly, etc., inorder to achieve desired goals of the patient care plan.

Thus, the illustrative embodiments provide various mechanisms forproviding actual personalized patient care plans based not only on acategorization of the patient based on their medical diagnosis anddemographic information, but also based on their own specific lifestyleinformation and lifestyle information obtained from third-party sources,e.g., information sources that provide information about a user'sgeographical surroundings, establishments in the user's geographicalsurroundings, event information sources, and the like. By personalizingthe patient's care plan to their specific lifestyle, the likelihood thatthe patient will adhere to the care plan and perform the actionsspecified in the care plan is increased. Essentially, the personalizedpatient care plan helps to instruct the patient how the patient canintegrate the care plan into their existing lifestyle without placingthe burden on the patient to perform the analysis and evaluation on howto achieve such integration.

Having generated a personalized patient care plan taking into accountthe patient's personal lifestyle, the illustrative embodiments furtherprovide mechanism for assisting and controlling the monitoring of apatient's adherence to the personalized care plan as well as assisthealth professionals, assessors, automated assessment systems, and thelike, in performing actions and initiating communications to maintainongoing treatment and care of the patient. Such mechanisms may involveevaluating the lifestyle information for the patient, the personalizedcare plan with its associated care plan actions, and determiningappropriate monitoring actions/communications to be performed, timing ofmonitoring actions/communications, communication modes to be utilized,content of such communications, and the like, so as to maximize apositive response from the patient. Examples of such monitoring actionsmay be interrogating health monitoring devices and/or applicationsassociated with the patient, e.g., wearable devices such as a FitBit™,pedometer, GPS device, applications running on a patient's smart phoneor other computing device, or the like, initiating a remindercommunication to be sent to the patient to remind them to perform anaction in accordance with their personalized patient care plan,scheduling a doctor's appointment for the patient and informing them ofthe appointment, initiating a call to the patient's telephone to discusstheir progress, or any other action that a human or automated assessmentsystem may perform to assist with the monitoring of the patient'sadherence to the patient's personalized patient care plan.

The particular monitoring actions to be employed are matched to thespecific personalized patient care plan that is associated with thepatient. That is, for each patient care plan action, there may be a setof one or more possible monitoring actions that may be associated withthat type of patient care plan action. Selection from amongst the one ormore possible monitoring actions may be performed based on an analysisof the patient's lifestyle information to determine the most appropriatemonitoring action that will not interfere with the patient's lifestyleand will most likely result in a positive response from the patient. Forexample, if it is determined that the patient's lifestyle is such thatthe patient eats breakfast at 8:30 a.m. and one of the patient care planactions is to eat oatmeal for breakfast three times a week, then amonitoring action may be selected that involves texting the patient witha message at 8:25 a.m., with the message having content that states“consider eating oatmeal for breakfast today.” Other options may be tocall the patient or send an electronic mail message but the patient'slifestyle information indicates that the patient is not a “morningperson” and thus, is unlikely to respond well to calls in the morningand is generally in a rush to go to work since the patient eatsbreakfast at 8:30 a.m. and needs to be at the office by 9:30 a.m.indicating little time for checking electronic mail.

As with the personalized patient care plan, the monitoring plan and itsmonitoring actions, as well as their timing, may be personalized to thepersonalized patient care plan and the specific patient's lifestyleinformation. For example, if the patient works in a manufacturingenvironment where noise levels are high, it is unlikely that the patientwill want to conduct a telephone conversation with a human assessor andis more likely to be responsive to textual communications. Thus, duringworking hours, monitoring actions may be restricted to textualcommunications, such as instant messaging or electronic mail. Similarly,if the patient works in a hospital, school, or other location wheredisturbances are to be minimized, communications may not be made duringtimes of the day where the patient is likely to be present in suchlocations. Furthermore, as another example, if it is known that thisparticular patient weighs himself and takes his blood sugar measurementseach morning at approximately 9:00 a.m., then a monitoring action may beto send a request to the electronic scale and/or blood sugar analysismechanism to request the results of that day's measurements.

Thus, monitoring plans and corresponding monitoring actions are selectedbased on the patient's personalized patient care plan, the patientactions specified in the personalized patient care plan, and thelifestyle information for the particular patient. It should beappreciated that as the patient care plan changes over time, themonitoring plan also changes to match the changes to the patient careplan. Hence, in embodiments where the patient's personalized patientcare plan is dynamically modified, such as in the case of dynamicchanges based on weather, temperature, availability of facilities orresources, etc., the monitoring plan may likewise be dynamicallymodified.

In an even further aspect of the illustrative embodiments, thegeneration of the personalized care plan, and thus, the patient actionsand monitoring actions of an assessor, may further take intoconsideration historical analysis of both the present patient and othersimilar patients with regard to previously prescribed patient care plansassociated with these patients and their relative success/failure atadhering to these previously prescribed patient care plans and/orindividual patient care plan actions that are part of these previouslyprescribed patient care plans. That is, historical analysis of patientinformation is performed across multiple patients to determine whichcare plans patients previously were able to adhere to, which care plans,and individual patient actions or tasks within patient care plans,resulted in successful outcomes for the patients, which resulted inunsuccessful outcomes for the patients, and generates a prediction as tothe best patient care plans, patient actions or tasks, etc. to be givento future patients having similar attributes. This will result inpatient care plans having tasks/actions for both the patient and theassessor that are tailored to the particular patient, as mentionedabove, but in which previous success of other similar patients is takeninto account when generating the personalized patient care plan. Thishistorical analysis can be performed in the aggregate over a pluralityof patients and/or on an individual basis based on what this particularpatient has shown success, or lack thereof, with in the past.

For example, if it is determined that diabetic patients that are female,in the age range of 40-45, and are smokers tend to have negative resultswhen their patient care plan involves strong cardiac exercise for 30minutes a day (i.e., the patient tends to fail to complete this task),then future prescribed patient care plans may adjust based on thishistorical analysis. For example, the future patient care plans mayreduce the requirement or substitute the requirement of the care plan,e.g., replace the patient action with one that requires mild cardiacexercise for 30 minutes a day. Alternatively, if it determined thatdiabetic patients that are female, in the age range of 40-45, and aresmokers tend have positive results when their patient care plan involvesdrinking coffee and eating oatmeal for breakfast, then this may be addedto future care plans for similar patients. Thus, adjustment of futurepatient care plans is made based on historical analysis of similarpatient care plans and the patient's own history indicating positiveresults and adherence to previous patient care plans, e.g., if thisparticular patient has a history of failing to perform stressfulexercise based patient actions in the past, then future patient careplans for this patient may be modified to not include stressfulexercised based patient actions.

It should be appreciated that this historical evaluation may beperformed at any point during the process of personalizing a patientcare plan as previously described above. Thus, for example, in oneillustrative embodiment, the historical analysis may be performed whengenerating the generalized patient care plan so as to identify thegeneral goals and corresponding general patient care plan actions thatpreviously have been most likely achieved by the current and otherpatients. In addition, either in the same or other illustrativeembodiments, the historical analysis may be performed when personalizingthe generic patient care plans based on the patient's lifestyleinformation. That is, historical analysis may be performed based on thepatient's previous personalized patient care plans to determine whattypes of physical exercise actions the patient has previously been ableto adhere to, which they have not been able to adhere to, or the like.In cases where similar patient care plan actions have not beenpreviously prescribed for this patient, patient care plan informationfor similar patients, such as in a cohort of patients having similardemographics and medical data, may be analyzed to identify the patientactions that similar patients have been able to adhere to and utilizethose as a basis for generating personalized patient actions in thepersonalized patient care plan for the present patient. Such actions maybe personalized to the current patient's lifestyle in the mannerpreviously described above. For example, assume that the general patientcare plan calls for 30 minutes of stressful exercise which the patienthas not been previously prescribed to perform, but similar patients havebeen able to adhere to 30 minutes of brisk walking a day and thus, thispatient action is used as a basis for generating the present patient'sgeneral patient care plan. This action may then be personalized to theparticular patient's lifestyle by generating specific personalizedpatient care plan actions for performing brisk walking at 8:00 a.m.,along Hyde Street, for 25 minutes and then 5 minutes of stair walking atwork on weekdays due to the patient working in a multi-story building.

In yet a further aspect of the illustrative embodiments, mechanisms areprovided for dynamically adjusting or modifying personalized patientcare plans based on a determined level of adherence to the personalizedpatient care plan, as determined from the monitoring actions performedand discussed above. That is, the patient's adherence to theirpersonalized patient care plan is monitored and determinations are madeas to whether the patient meets the goals set forth in the personalizedpatient care plan and/or performs the patient actions in thepersonalized patient care plan. If the patient does not meet therequirements of one or more goals in the patient care plan, analternative goal determination logic is employed to determine analternative goal that the patient is more likely to be able toaccomplish. This determination may be made based on the patient's actualprogress towards attaining the original goal, the importance and type ofthe goal to the overall personalized patient care plan, e.g.,adjustments to medication may not be able to be made depending on theparticular care plan, and a pre-determined inter-changeability of thegoals. In some cases, one goal may be adjusted in one direction, or by afirst adjustment metric, and another in a different direction, or by asecond adjustment metric, so as to balance the patient's ability toachieve a missed goal with an alternative goal while maintaining overallresults that are to be generated, e.g., physical activity goal may bereduced while dietary goals may be increased so that the balanceachieves the same overall effect. In this way, the patient'spersonalized patient care plan is further optimized for the particularpatient based on the achievability of the goals for that particularpatient.

In addition to finding alternative goals for a personalized patient careplan, alternative patient actions, and thus corresponding monitoringactions, may be identified for patient actions in the patient care planthat the patient has not been able to adhere to. In some illustrativeembodiments, the determination of alternative care plan actions forperforming the alternative goals may be based on a historical analysisof patient actions in other patient care plans that the patient and/orsimilar patients have undergone. This historical analysis may identifyother similar patient actions that achieved similar results to thepatient actions that the patient is found to not be able to achieve inthe patient's current personalized patient care plan.

Thus, in general, as can be seen from the above description andexamples, the mechanisms of the illustrative embodiments combineinformation about a patient's medical condition, medical history,lifestyle information, geographical location(s), facilities located inthese geographical locations(s), products and services available inthese geographical location(s), desired goals of the care plan, andother lifestyle information, and personalizes the patient care plan tothe patient's particular medical condition, particular lifestyle, andavailable facilities and resources to provide a specific personalizedpatient care plan for this specific patient that is not widelyapplicable to generalized categories of patients.

This information may further be used to personalize the assessmentactivities to be performed by the assessment system/personnel andinfluence the timing, communication modes, and monitoring actionsperformed. That is, based on the particular care plan goals and careplan actions that are part of the patient's care plan, thesegoals/actions may be paired with monitoring actions to be taken by anassessor, e.g., a medical professional, other individual whose duty itis to monitor and interface with patients to ensure that they arefollowing a prescribed care plan, or automated system. The monitoringactions may likewise be personalized based on the patient's lifestyleinformation, geographical information, available products and servicesin the patient's geographical area(s) of interest (e.g., home, work,etc.), and the like. The assessment tasks may be automatically orsemi-automatically performed so as to gather information for monitoringthe patient's adherence to the personalized patient care plan and eitherautomatically or semi-automatically adjust the personalized patient careplan accordingly, send notifications to the patient, notify the doctor,or perform some other desired actions for maximizing the probabilitythat the patient will maintain adherence to the personalized patientcare plan.

It should be appreciated that the personalized patient care plans, andthe personalized patient care plan actions (patient actions performed bythe patient and monitoring actions performed by the assessor), may bedynamically adjusted based on the patient's current environmentalconditions, changes in schedule, determined deviations from the careplan, and other dynamic conditions that may interfere or otherwiserequire modification, either temporarily or permanently, of thepatient's personalized patient care plan. As noted above, such factorsas weather conditions, temperature conditions, resource availability(e.g., gym is closed), and the like may require temporary modificationsto a patient's personalized patient care plan. Other factors, such asthe patient moving to a new location, obtaining a new place ofemployment, or the like, may require more permanent modifications to thepatient's personalized patient care plan. Such factors may be identifiedand corresponding modifications initiated taking into account the newtemporary/permanent lifestyle changes of the patient.

In some illustrative embodiments, the analysis of the various patientinformation for generating of a personalized care plan, modification ofpersonalized care plans, determining appropriate actions to perform,sending communications and selecting communication modes, and the like,may be performed utilizing a hierarchical system of clinical rulesdefined based on standardized guidelines from health care providers,health care payment providers, best practices determined by subjectmatter experts, e.g., physicians and other medical personnel, thegeneral knowledge of subject matter experts, and the like. In someembodiments, the clinical rules may be generated based on naturallanguage processing of natural language text defining these guidelines,best practices, and other knowledge of subject matter experts. Agraphical user interface may be provided for facilitating creation ofthe clinical rules utilizing an object oriented engine user interfaceelements. The graphical user interface permits the creation of suchclinical rules without having to have expert medical knowledge. Theclinical rules may be applied to a patient registry comprisingelectronic medical records, demographics information, lifestyleinformation, and the like, to classify patients into various cohortsbased on their characteristics. These cohorts may be correlated topersonalized care plans, actions or requirements to be part ofpersonalized care plans, communication modes to utilize, and the like.Moreover, the clinical rules may be applied to patient information todetermined care opportunities and what actions to be performed toimprove the care provided to patients.

From the above general overview of the mechanisms of the illustrativeembodiments, it is clear that the illustrative embodiments areimplemented in a computing system environment and thus, the presentinvention may be implemented as a data processing system, a methodimplemented in a data processing system, and/or a computer programproduct that, when executed by one or more processors of one or morecomputing devices, causes the processor(s) to perform operations asdescribed herein with regard to one or more of the illustrativeembodiments. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

As shown in the figures, and described hereafter, one or more computingdevices comprising a distributed data processing system, may bespecifically configured to implement a personalized patient care plansystem in accordance with one or more of the illustrative embodiments.The configuring of the computing device(s) may comprise the providing ofapplication specific hardware, firmware, or the like to facilitate theperformance of the operations and generation of the outputs describedherein with regard to the illustrative embodiments. The configuring ofthe computing device(s) may also, or alternatively, comprise theproviding of software applications stored in one or more storage devicesand loaded into memory of a computing device for causing one or morehardware processors of the computing device to execute the softwareapplications that configure the processors to perform the operations andgenerate the outputs described herein with regard to the illustrativeembodiments. Moreover, any combination of application specific hardware,firmware, software applications executed on hardware, or the like, maybe used without departing from the spirit and scope of the illustrativeembodiments.

It should be appreciated that once the computing device is configured inone of these ways, the computing device becomes a specialized computingdevice specifically configured to implement the mechanisms of one ormore of the illustrative embodiments and is not a general purposecomputing device. Moreover, as described hereafter, the implementationof the mechanisms of the illustrative embodiments improves thefunctionality of the computing device(s) and provides a useful andconcrete result that facilitates creation, monitoring, and adjustingpersonalized patient care plans based on personalized lifestyleinformation and assessment of patient adherence to the personalizedpatient care plan.

As mentioned above, the mechanisms of the illustrative embodiments maybe implemented in many different types of data processing systems, bothstand-alone and distributed. Some illustrative embodiments implement themechanisms described herein in a cloud computing environment. It shouldbe understood in advance that although a detailed description on cloudcomputing is included herein, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed. For convenience, the Detailed Description includes thefollowing definitions which have been derived from the “Draft NISTWorking Definition of Cloud Computing” by Peter Mell and Tim Grance,dated Oct. 7, 2009.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models. Characteristics of a cloud model are asfollows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service models of a cloud model are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment models of a cloud model are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes. A node in a cloud computing network isa computing device, including, but not limited to, personal computersystems, server computer systems, thin clients, thick clients, hand-heldor laptop devices, multiprocessor systems, microprocessor-based systems,set top boxes, programmable consumer electronics, network PCs,minicomputer systems, mainframe computer systems, and distributed cloudcomputing environments that include any of the above systems or devices,and the like. A cloud computing node is capable of being implementedand/or performing any of the functionality set forth hereinabove.

Personalized Care Plan Generation and Monitoring

FIG. 1 is a block diagram illustrating a cloud computing system 100 forproviding software as a service, where a server provides applicationsand stores data for multiple clients in databases according to oneexample embodiment of the invention. The networked system 100 includes aserver 102 and a client computer 132. The server 102 and client 132 areconnected to each other via a network 130, and may be connected to othercomputers via the network 130. In general, the network 130 may be atelecommunications network and/or a wide area network (WAN). In aparticular embodiment, the network 130 is the Internet.

The server 102 generally includes a processor 104 connected via a bus115 to a memory 106, a network interface device 124, a storage 108, aninput device 126, and an output device 128. The server 102 is generallyunder the control of an operating system 107. Examples of operatingsystems include UNIX, versions of the Microsoft Windows™ operatingsystem, and distributions of the Linux™ operating system. Moregenerally, any operating system supporting the functions disclosedherein may be used. The processor 104 is included to be representativeof a single CPU, multiple CPUs, a single CPU having multiple processingcores, and the like. Similarly, the memory 106 may be a random accessmemory. While the memory 106 is shown as a single identity, it should beunderstood that the memory 106 may comprise a plurality of modules, andthat the memory 106 may exist at multiple levels, from high speedregisters and caches to lower speed but larger DRAM chips. The networkinterface device 124 may be any type of network communications deviceallowing the server 102 to communicate with other computers via thenetwork 130.

The storage 108 may be a persistent storage device. Although the storage108 is shown as a single unit, the storage 108 may be a combination offixed and/or removable storage devices, such as fixed disc drives, solidstate drives, floppy disc drives, tape drives, removable memory cards oroptical storage. The memory 106 and the storage 108 may be part of onevirtual address space spanning multiple primary and secondary storagedevices.

As shown, the storage 108 of the server contains a plurality ofdatabases. In this particular drawing, four databases are shown,although any number of databases may be stored in the storage 108 ofserver 102. Storage 108 is shown as containing databases numbered 118,120, and 122, each corresponding to different types of patient relateddata, e.g., electronic medical records (EMRs) and demographicinformation, lifestyle information, treatment guidelines, personalizedpatient care plans, and the like, for facilitating the operations of theillustrative embodiments with regard to personalized patient care plancreation, monitoring, and modification. Storage 108 is also showncontaining metadata repository 125, which stores identificationinformation, pointers, system policies, and any other relevantinformation that describes the data stored in the various databases andfacilitates processing and accessing the databases.

The input device 126 may be any device for providing input to the server102. For example, a keyboard and/or a mouse may be used. The outputdevice 128 may be any device for providing output to a user of theserver 102. For example, the output device 108 may be any conventionaldisplay screen or set of speakers. Although shown separately from theinput device 126, the output device 128 and input device 126 may becombined. For example, a display screen with an integrated touch-screenmay be used.

As shown, the memory 106 of the server 102 includes a personalizedpatient care plan application 110 configured to provide a plurality ofservices to users via the network 130. As shown, the memory 106 ofserver 102 also contains a database management system (DBMS) 112configured to manage a plurality of databases contained in the storage108 of the server 102. The memory 106 of server 102 also contains a webserver 114, which performs traditional web service functions, and mayalso provide application server functions (e.g. a J2EE applicationserver) as runtime environments for different applications, such as thepersonalized patient care plan application 110.

As shown, client computer 132 contains a processor 134, memory 136,operating system 138, storage 142, network interface 144, input device146, and output device 148, according to an embodiment of the invention.The description and functionality of these components is the same as theequivalent components described in reference to server 102. As shown,the memory 136 of client computer 132 also contains web browser 140,which is used to access services provided by server 102 in someembodiments.

The particular description in FIG. 1 is for illustrative purposes onlyand it should be understood that the invention is not limited tospecific described embodiments, and any combination is contemplated toimplement and practice the invention. Although FIG. 1 depicts a singleserver 102, embodiments of the invention contemplate any number ofservers for providing the services and functionality described herein.Furthermore, although depicted together in server 102 in FIG. 1, theservices and functions of the personalized patient care plan application110 may be housed in separate physical servers, or separate virtualservers within the same server. The personalized patient care planapplication 110, in some embodiments, may be deployed in multipleinstances in a computing cluster. As is known to those of ordinary skillin the art, the modules performing their respective functions for thepersonalized patient care plan application 110 may be housed in the sameserver, on different servers, or any combination thereof. The items instorage, such as metadata repository 125, databases 118, 120, and 122,may also be stored in the same server, on different servers, or in anycombination thereof, and may also reside on the same or differentservers as the application modules.

Referring now to FIG. 2, another perspective of an illustrative cloudcomputing environment 250 is depicted. As shown, cloud computingenvironment 250 comprises one or more cloud computing nodes 210, whichmay include servers such as server 102 in FIG. 1, with which localcomputing devices used by cloud consumers, such as, for example,personal digital assistant (PDA) or cellular telephone 254A, desktopcomputer 254B, laptop computer 254D, and/or automobile computer system254N may communicate. Nodes 210 may communicate with one another. Acomputing node 210 may have the same attributes as server 102 and clientcomputer 132, each of which may be computing nodes 210 in a cloudcomputing environment. They may be grouped (not shown) physically orvirtually, in one or more networks, such as Private, Community, Public,or Hybrid clouds as described hereinabove, or a combination thereof.This allows cloud computing environment 250 to offer infrastructure,platforms and/or software as services for which a cloud consumer doesnot need to maintain resources on a local computing device. It isunderstood that the types of computing devices 254A-N shown in FIG. 2are intended to be illustrative only and that computing nodes 210 andcloud computing environment 250 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 250 (FIG. 2) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided.

The hardware and software layer 360 includes hardware and softwarecomponents. Examples of hardware components include mainframes, in oneexample IBM™ zSeries™ systems; RISC (Reduced Instruction Set Computer)architecture based servers, in one example IBM pSeries™ systems; IBMxSeries™ systems; IBM BladeCenter™ systems; storage devices; networksand networking components. Examples of software components includenetwork application server software, in one example IBM Web Sphere™application server software; and database software, in one example IBMDB2™ database software. (IBM, zSeries, pSeries, xSeries, BladeCenter,WebSphere, and DB2 are trademarks of International Business MachinesCorporation registered in many jurisdictions worldwide.).

The virtualization layer 362 provides an abstraction layer from whichthe following examples of virtual entities may be provided: virtualservers; virtual storage; virtual networks, including virtual privatenetworks; virtual applications and operating systems; and virtualclients. In one example, management layer 364 may provide the functionsdescribed below. Resource provisioning provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricingprovide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal provides access to the cloud computing environment forconsumers and system administrators. Service level management providescloud computing resource allocation and management such that requiredservice levels are met. Service Level Agreement (SLA) planning andfulfillment provide pre-arrangement for, and procurement of, cloudcomputing resources for which a future requirement is anticipated inaccordance with an SLA.

Workloads layer 366 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation; software development and lifecycle management; virtualclassroom education delivery; data analytics processing; transactionprocessing; and, in accordance with the mechanisms of the illustrativeembodiments, a personalized patient care plan creation and monitoringfunctionality.

As discussed above, the illustrative embodiments provide a personalizedpatient care plan creation and monitoring system which may beimplemented in various types of data processing systems. FIG. 4 is anexample block diagram illustrating the primary operational elements ofsuch a personalized patient care plan creation and monitoring system inaccordance with one illustrative embodiment. The operational elementsshown in FIG. 4 may be implemented as specialized hardware elements,software executing on hardware elements, or any combination ofspecialized hardware elements and software executing on hardwareelements without departing from the spirit and scope of the presentinvention.

As shown in FIG. 4, a personalized patient care plan creation andmonitoring (PCPCM) system 410 comprises information source interfaces411, demographic and medical data analysis engine 412, lifestyle dataanalysis engine 413, personalized care plan creation/update engine 414,and personalized care plan monitor engine 415. In addition, the PCPCMsystem 410 maintains a personalized patient care plan database 416 thatstores data corresponding to the personalized patient care plansgenerated for various patients and a patient cohort database 417 thatstores cohort association information for various patients havingsimilar characteristics, e.g., demographics and/or medical data. Entriesin the personalized patient care plan database 416 may be associatedwith entries in the patient cohort database 417.

A personalization resources storage 418 provides resources utilized bythe personalized care plan creation/update engine 414 for identify andcorrelate demographic, medical, lifestyle information, and generalpatient care plan information associated with a patient into a series ofpersonalized patient care plan actions and corresponding monitor actionsfor an assessor. The personalization resources storage 418 may comprisesystems of rules, patterns, equations, algorithms, and various othertypes of logic that codify or otherwise implement functions forselecting and deciding how to personalize a general set of goals andactions in a general patient care plan to a personalized patient careplan. These rules, patterns, equations, algorithms, and the like, may bedeveloped over time by subject matter experts, automatically identifiedby automated systems, such as natural language processing systems, orthe like. For example, such automated and manual based mechanisms may beprovided as part of a resource generating engine 419 described ingreater detail hereafter.

The rules, patterns, equations, algorithms, etc., may be applied to thelarge set of demographic, medical, and lifestyle information obtainedfor the patient to obtain an automatically generated personalizedpatient care plan which may then be presented to a subject matterexpert, such as a doctor, nurse, other medical professional, or thelike, for confirmation before prescribing the personalized patient careplan to the patient. It should be appreciated that the resources 418 mayfurther be utilized by the personalized care plan monitor engine 415when monitoring adherence to a personalized patient care plan anddetermining modifications to the personalize patient care plan based ondetermined levels of adherence, as discussed hereafter. Moreover, theresources 418 may be used to determine appropriate actions forinteracting with patients, care providers, payment providers, and thelike. In some illustrative embodiments, the resources 418 may be used toassociate patients in a patient registry, which may comprise EMR anddemographics courses 420, lifestyle information sources, and the like,with particular patient cohorts, where a patient cohort is a grouping ofpatients having the same or similar characteristics.

The information source interfaces 411 provides a data communicationinterface through which patient data may be obtained from varioussources including electronic medical records (EMRs) data source 420,patient supplied lifestyle data source 421, environment lifestyleinformation source 422, geospatial lifestyle information source 423,establishment lifestyle information source 424, and other variouslifestyle information data sources 425. Moreover, the interfaces 411comprise interfaces for obtaining patient care plan guidelinesinformation from source 426. The EMR data source 420 may comprisevarious sources of electronic medical records including individualdoctor medical practice systems, hospital computing systems, medical labcomputing systems, personal patient devices for monitoring health of thepatient, dietary information, and/or activity information of thepatient, or any other source of medical data that represents aparticular patient's current and historical medical condition. The EMRdata source 420 may further comprise data representing the patientdemographics since such information is typically gathered by providersof such medical data.

The patient supplied lifestyle data source 421 may be a database and/orcomputing system that gathers and stores information from the patientindicating the patient's response to questionnaires, presented eitherphysically and then entered through a data entry process or presentedelectronically and gathered automatically, directed to the patient'slifestyle, preferences, and the like. For example, questions in thequestionnaire may ask questions about the patient's personal dailyschedule, home and work environment conditions, family information,preferences regarding food types, exercise types, times of the day forperforming actions, and the like. This information is gathered directlyfrom the patient but may not cover all aspects of the patient'slifestyle. This lifestyle information may be augmented by otherlifestyle information gathered from other sources which may bethird-party lifestyle information sources. These third-party lifestyleinformation may comprise information from commercial and governmentalcomputing systems, databases, and the like, that characterize thepatient's environment, availability to resources (e.g.,products/services/facilities), etc.

In the depicted example, third-party lifestyle information sourcescomprise environment lifestyle information source 422, geospatiallifestyle information source 423, establishment lifestyle informationsource 424, and other various lifestyle information data sources 425.Examples of environment lifestyle information source 422 compriseweather information services, air quality information services, trafficinformation services, crime information services, governmentalinformation services regarding public utilities, or any otherenvironment lifestyle information source 424. As one example, athird-party geospatial lifestyle information source 423 may comprise aglobal positioning system (GPS) source that identifies the patient'sassociated locations, e.g., home, work, etc., and identifiesestablishments around those locations that provide resources that are ofinterest to the patient's lifestyle and potentially of interest ingenerating a patient care plan. For example, as mentioned above,specialty grocery stores, vitamin stores, pharmacies, restaurants, gyms,walking paths, parks, recreational areas, community pools, and the like,may be identified based on a GPS system and its associated databases ofinformation.

The information from the geospatial lifestyle information source 423 maybe used to request or lookup establishment information in theestablishment lifestyle information source 424. For example, if thegeospatial lifestyle information source 423 identifies an establishmenttype and specific identity of a particular establishment, thisinformation may be used to request or lookup other third-party lifestyleinformation for the establishment in the establishment lifestyleinformation source 424, e.g., the establishment's website, an industrybased website, blogs, commercial establishment information repository,or the like, to retrieve specific information about the identifiedestablishment, e.g., menu items, nutrition information, hours ofoperation, and the like. Similarly, other third-party lifestyleinformation source 425 may provide information for correlation withpatient care plan actions/tasks including hours of operations,products/services provided, distance from the patient's locations, andthe like.

The lifestyle information obtained from the lifestyle informationsources 421-425 may be combined with EMR and demographics informationfor a patient to generate a patient registry record of a patientregistry. The patient registry may comprise information for a pluralityof patients which may be operated on by the PCPCM system 410 to identifypersonalized patient care plans for patients, associated patients withpatient cohorts, identify potential opportunities for improving care ofpatients in accordance with clinical rules applied to the patientinformation in the patient registry records, and the like.

The patient care plan guidelines source 426 provides informationregarding the preferred treatments for various medical conditions ormaladies in association with patient characteristics. These guidelinesare generally associated with demographic and medical information aboutpatients and provide general guidelines as to who qualifies for atreatment, or patient care plan, and who does not based on their medicalinformation and demographic information. The patient care planguidelines provide an initial basis for determining a general patientcare plan for a patient which may then be personalized to the particularpatient based on the lifestyle information specific to that particularpatient. The patient care plan guidelines from the patient care planguidelines sources 426 may be provided in a natural language text formwhich may be processed by natural language processing mechanisms of aresource generation engine 490 to generate clinical rules fordetermining actions to be performed, communications to be sent, portionsof personal care plans to be applied to a patient, or the like. Theseautomated mechanisms may be used in addition to, or in replacement of,manual processes of subject matter experts for generating clinical rulesas part of the resources database 418.

The PCPCM system 410 may receive a request to generate a personalizedpatient care plan for a particular patient, such as from a physician'scomputing system, a patient computing system, or the like, whichinitiates the processes of the PCPCM system 410 including retrievinginformation about the specified patient from the EMR sources 420. TheEMR sources 420 provide patient demographic and medical data, gatheredfrom questionnaires, electronic medical records, and the like, to themedical data analysis engine 412 which analyzes the received data andextracts the necessary data for generating patient care plan from thedemographic and medical data received. This information is then used asa basis for submitting a request to the patient care plan guidelinessource 426 to retrieve patient care plan guidelines for the patient'sspecific demographics and medical data, e.g., the patient is a 40 yearold female diagnosed with type 2 diabetes and thus, correspondingpatient care plan guidelines for this combination of patientdemographics and medical condition are retrieved from the patient careplan guidelines source 426. Alternatively, the patient care planguidelines from source 426 may be previously ingested and converted toapplicable clinical rules, either through an automated process, manualprocess, or combination of manual and automated processes. Theseclinical rules that codify the patient care plan guidelines may bestored in the resources database 418, for example.

The retrieved patient care plan guidelines and/or clinical rules areused along with the demographics and medical data for the patient togenerate a baseline patient care plan based on an initial diagnosis ofthe patient's medical condition, one or more categorizations of thepatient based on the collected demographic and medical data, theestablished patient care plan guidelines, and goals to be achieved bythe patient care plan, such as may be specified in the establishedpatient care plan guidelines and/or patient medical data. Theseoperations are performed by the PCPCM system 410 utilizing the resources418 which provide the clinical rules, logic, equations, algorithms andother logic for evaluating patient information and correlating thatinformation with a patient care plan that comprises patient actions tobe performed by the patient and monitoring actions to be performed bythe assessor. It should be appreciated that based on the demographicinformation about the patient and the patient's medical data, only ageneral patient care plan is generated at this point.

The resulting general patient care plan generated by the personalizedcare plan creation/update engine 414 is then personalized based on thelifestyle information for the patient obtained via the lifestyle dataanalysis engine 413 convert the general patient care plan to apersonalized patient care plan for the specific patient based on theirown unique combination of lifestyle information. The lifestyle dataanalysis engine 413 obtains the lifestyle information from the varioussources 421-425 and performs analysis to generate lifestyle inferencesfrom the lifestyle data. Again, resources may be provided in theresources storage 418 for providing logic, algorithms, clinical rules,patterns, etc., for drawing these inferences from the received lifestyleinformation. For example, from schedule data for the patient, geospatiallifestyle information, environment lifestyle information, and the likefor the patient, it may be determined, based on clinical rules,patterns, algorithms, and the like, that the patient has a sedentaryoccupation, works in a multi-story building that has a gym, lives in anarea with access to parks and walking paths, and the like. As oneexample, the lifestyle information may indicate that the patient'soccupation is a lawyer. From that information, a lookup of theoccupation in an occupation database provided in the resources 418 mayindicate characteristics of the occupation including characteristics of“stressful”, “sedentary”, and “long hours” which provides lifestyleinferences about the patient that can be utilized by rules in theresources 418 implemented by the personalized care plan creation/updateengine 414 to personalize the general patient actions in the generalpatient care plan to the particular patient. Various analysis oflifestyle information may be used to extract such inferences from thedata which can then be used to personalize a general patient care plan.

As mentioned above, lifestyle information data is obtained from varioussources 421-425 to obtain an overall representation of the lifestyle ofthe patient. These third-party lifestyle information sources 422-425 mayprovide lifestyle information that is combined with lifestyleinformation provided by the patient himself/herself 421 for analysis toidentify the types of personalized care plan actions to be used with thepatient's care plan, the timing of the actions, and the types and timingof patient care plan monitoring and management actions to be performedby an assessor, e.g., a human assessor, automated assessment system, ora combination of human and automated assessment mechanisms. Thus, theselection of patient care plan actions (i.e. patient actions andmonitoring actions) is based on the general patient care plan goals, thegeneral patient care plan actions to be performed, and thepersonalization of these general patient care plan actions to thespecific lifestyle of the patient.

Various lifestyle information analysis logic is provided in thelifestyle data analysis engine 413 to evaluate and classify thepatient's lifestyle in accordance with a number of defined lifestylecategories. For example, the patient's lifestyle may be categorizedaccording to level of physical activity, level of availability tohealthy food sources, quality of home and work environment (lighting,air quality, quietness, safety, etc.), level of access to exercisefacilities, various qualitative aspects of the patient's home and worklife, and the like. From these categories, a more specific patient careplan is generated to achieve the goals and actions of the genericpatient care plan. Non-limiting examples of ways in which generalpatient care plans may be personalized based on lifestyle informationhave been provided above. Such personalization may be performed by thepersonalized care plan creation/update engine 414.

It should be appreciated that the lifestyle information and/or resources418 may comprise various reference resources from which the mechanismsof the PCPCM system 410 may obtain information for making decisions asto how to personalize the patient care plan actions (patient actions andmonitoring actions). Such reference resources may comprise druginformation repositories, food nutrition repositories, exerciseinformation repositories, medical procedure repositories, and the like.The “reference” resources differ from other lifestyle informationsources in that these “reference” resources tend to be universal for allpatients. Such reference resources may be utilized, for example, toassist in determining drug affects on other lifestyle characteristics(e.g., drugs that make one lethargic, prone to disorientation, or thelike), selecting foods whose nutritional content falls within thedesired goals of a patient care plan, selecting exercises that generatea desired level of activity within a given period of time, and the like.

It should be appreciated that in addition to the evaluation of thepatient's demographic, medical, and lifestyle information, thepersonalized care plan creation/update engine 414 may evaluate thehistorical personalized care plan information for a patient and forother similar patients to determine appropriate patient actions toinclude in a personalized care plan. For example, the personalized careplan creation/update engine 414 may look to a history of personalizedcare plans created for this patient, as may be maintained in thepersonalized patient care plan database 416 in association with anidentifier of the patient, to determine what patient actions the patientwas able to successfully complete in previously prescribed personalizedpatient care plans and use this information to select those same patientactions for a current personalized patient care plan should the currentpersonalized patient care plan have similar goals, general patientactions, and the like that the previously successful patient actionswould satisfy. Thus, when selecting personalized patient actions toinclude in the personalized patient care plan, different weightings maybe applied to patient actions based on whether or not they werepreviously prescribed to this patient, whether or not they werepreviously successfully completed by the patient in previouslyprescribed personalized patient care plans, and a level of successful ornon-successful completion of the patient action in previously prescribedpersonalized patient care plans. A highest ranking patient action,amongst the possible patient actions, may then be selected for inclusionin the personalized patient care plan.

In addition, the personalized care plan creation/update engine 414 mayretrieve information from the patient cohort database 417 to classifythe patient into a patient cohort. The patient cohort is a grouping ofpatients that have similar characteristics, e.g., similar demographics,similar medical diagnoses, etc. Patient cohorts may be generated usingany known or later developed grouping mechanism. One example mechanismmay be using a clustering algorithm that clusters patients based on keycharacteristics of the patient, e.g., age, gender, race, medicaldiagnosis, etc. As another example, rules in the resources database 418may be defined for application to patient information in the EMR anddemographics sources 420 and lifestyle information sources foridentifying patients that have specified characteristics, e.g., patientsthat have diabetes and are in the age range of 18-45.

With regard to the illustrative embodiments, the present patient may begrouped into a patient cohort and the other members of the patientcohort may be evaluated to identify patient actions that the othermembers were able to successfully complete as part of their individualpersonalized patient care plans. These patient actions may then beprovided for use in generating the personalized patient care plan forthe present patient, with appropriate weightings applied to rank thesepatient actions relative to other patient actions for purposes ofselection as discussed above.

Thus, the PCPCM system 410 provides the various mechanisms for providingactual personalized patient care plans based not only on acategorization of the patient based on their medical diagnosis anddemographic information, but also based on their own specific lifestyleinformation and lifestyle information obtained from third-party sources.In addition, the PCPCM system 410 further provides the mechanisms forgenerating, as part of the personalized patient care plan, monitoringactions to be performed by an assessor in monitoring the patient'sperformance of the patient actions of the personalized patient careplan. That is, based on the creation of the series of patient actions tobe performed by the patient over a designated period of time, e.g.,daily, weekly, monthly, etc., corresponding monitoring actions areidentified by the personalized care plan monitor engine 415 using theresources 418. The resources 418 may comprise rules, logic, patterns,algorithms, etc. that match monitoring actions to types of patientactions. Based on timing information for the patient actions,preferences specified by the patient in the patient supplied lifestyleinformation 421, and the like, these monitoring actions may be scheduledas part of the personalized patient care plan monitor, e.g., every daythe patient wakes at 7:00 a.m. and eats breakfast at 7:30 a.m.,therefore schedule a monitoring action at 7:25 a.m. to send a textmessage to the patient's communication device to inform the patient thatthey should eat bran flakes for breakfast on Monday, Wednesday, andFriday of the week. It should be appreciated that not every patientaction needs to have a corresponding monitoring action and thatmonitoring actions may be schedule for only a subset of the patientactions which are determined to be of most value in assisting thepatient with adherence to the personalized patient care plan.

Thus, the resulting personalized patient care plan comprises patientactions to be performed by the patient, and corresponding monitoringactions to be performed by the assessor. Having generated a personalizedpatient care plan (PPCP) taking into account the patient's personallifestyle, the PCPCM system 410 outputs the personalized patient careplan 419 to the requestor system 440 for use by the patient 442 inperforming the patient actions of the personalized patient care plan. Inaddition, as noted above, the personalized patient care plan 419 furthercomprises monitoring actions that are to be performed by an assessor viaassessor systems 430, which may be a human being utilizingcommunications and/or computing equipment 432-436 to perform theirmonitoring actions, an automated system 436 that automatically performsmonitoring actions, or a combination of human and automated systems. Thepersonalized patient care plan 419 is output to the assessor system(s)430 such that the assessor may utilize the monitoring actions in thepersonalized patient care plan 419 to monitor and evaluate the patient'sperformance of the patient actions.

In monitoring the patient 442 and the patient's adherence to thepersonalized patient care plan 419, the assessor system(s) 430 mayobtain feedback information from various patient systems 441 including ahealth/activity monitor system 444, communication device(s) 446, onlinefeedback system(s) 448, or the like. Examples of health/activity monitorsystem 444 include wearable devices, such as a FitBit™, iFit™ FitnessTracker, pedometers, medical equipment with data connectivity to one ormore networks via wired or wireless data communication links, or thelike. Examples of communication device(s) 446 may include smart phoneswith applications for communication via data networks to log health andactivity data for the patient 442, conventional phones through which ahuman or automated mechanism places calls to the patient 442, or thelike. Examples of online feedback system(s) 448 include websites fortracking a patient's medical condition including online food logs,weight monitoring services, and other health and activity monitoringsystems. Any systems that facilitate monitoring and/or communicationwith an assessor may be used as part of the patient system(s) 441without departing from the spirit and scope of the illustrativeembodiments.

Examples of monitoring actions performed by the assessor system(s) 430may include interrogating the health/activity monitoring devices and/orapplications executing on the communication devices 446 or onlinefeedback system(s) 448 associated with the patient, and initiating areminder communication to be sent to the patient's communication device446 via the assessor communication device 434 to remind the patient 442to perform an action in accordance with their personalized patient careplan 419, scheduling a doctor's appointment for the patient andinforming them of the appointment, initiating a call to the patient'scommunication device 446 to discuss their progress, or any other actionthat a human or automated assessment system 436 may perform to assistwith the monitoring of the patient's adherence to the patients'personalized patient care plan 419. Moreover, results of the monitoringmay be returned to the PCPCM system 410 for use in modifying thepersonalized patient care plan 419 based on the patient's determinedlevel of adherence to the personalized patient care plan 419.

In response to monitoring results and feedback gathered by the assessorsystem(s) 430, and provided back to the PCPCM system 410, thepersonalized care plan creation/update engine 414 may dynamically adjustor modify the personalized patient care plan 419 based on a determinedlevel of adherence to the personalized patient care plan 419. That is,the patient's adherence to their personalized patient care plan 419 ismonitored via the assessor system(s) 430 and the patient system(s) 441,and determinations are made as to whether the patient meets the goalsset forth in the personalized patient care plan 419 and/or performs thepatient actions in the personalized patient care plan 419. If thepatient does not meet the requirements of one or more goals in thepatient care plan 419, an alternative goal determination logic of thepersonalized care plan creation/update engine 414 is employed todetermine an alternative goal that the patient is more likely to be ableto accomplish. This determination may be made based on the patient'sactual progress towards attaining the original goal, the importance andtype of the goal to the overall personalized patient care plan, e.g.,adjustments to medication may not be able to be made depending on theparticular care plan, and a pre-determined inter-changeability of thegoals. These determinations may be made in a similar manner aspreviously described above with regard to the original generation of thepersonalized patient care plan utilizing the resources 418 and the like,with the adherence feedback and monitoring data being used as additionallifestyle information for influencing the selection of patient actionsand corresponding monitoring actions.

In some cases, one goal may be adjusted in one direction and another ina different direction so as to balance the patient's ability to achievea missed goal with an alternative goal while maintaining overall resultsthat are to be generated, e.g., physical activity goal may be reducedwhile dietary goals may be increased so that the balance achieves thesame overall effect. In some illustrative embodiments, the determinationof alternative patient actions for performing the alternative goals maybe based on a historical analysis of patient actions in other patientcare plans that the patient and/or similar patients in the patient'scohort have undergone. This historical analysis may identify othersimilar patient actions that achieved similar results to the patientactions that the patient is found to not be able to achieve in thepatient's current personalized patient care plan. Such historicalanalysis may be performed in a similar manner as previously describedabove but with a focus on patient actions that were not achieved by thepatient 442 in the PPCP 419.

It should be appreciated that the patient systems may further comprisesystems for identifying the current location, environmental conditions,changes in a schedule, and the like, for use by the assessor systems 430in providing feedback to the PCPCM system 410 to adjust the PPCP 419 forthe patient's current location and environment. That is, the PPCP 419may be dynamically adjusted based on the patient's current environmentalconditions, changes in schedule, determined deviations from the careplan, and other dynamic conditions that may interfere or otherwiserequire modification, either temporarily or permanently, of thepatient's personalized patient care plan. As noted above, such factorsas weather conditions, temperature conditions, resource availability(e.g., gym is closed), and the like may require temporary modificationsto a patient's personalized patient care plan. Other factors, such asthe patient moving to a new location, obtaining a new place ofemployment, or the like, may require more permanent modifications to thepatient's personalized patient care plan. Such factors may be identifiedand corresponding modifications initiated taking into account the newtemporary/permanent lifestyle changes of the patient.

FIG. 5 is a flowchart outlining an example operation for creating apersonalized patient care plan in accordance with one illustrativeembodiment. As shown in FIG. 5, the operation comprises receiving arequest (Personalized Patient Care Plan (PPCP) request) for the creationof a personalized patient care plan specifically identifying a patientfor which the personalized patient care plan is to be created (step510). EMR and demographic information is retrieved for the patient (step520) and used to retrieve one or more patient care plan guidelinescorresponding to the patient's characteristics (step 530). A generalizedpatient care plan (PCP) is generated for the patient based on theretrieved PCP guidelines and the patient's demographics and medicalinformation (step 540).

Patient specific lifestyle information is retrieved for the patient froma plurality of different lifestyle information sources (step 550).Moreover, in some illustrative embodiments, a historical analysis isperformed on patient actions in previously prescribed PCPs for thispatient and similar patients (such as patients in a same cohort) toidentify patient actions that are ones that the patient is likely to beable to adhere to and weight them more heavily during a selectionprocess (step 560). A personalized PCP is generated based on thegeneralized PCP as a basis which is then customized and personalized tothe specific patient using the retrieved lifestyle information, thehistorical analysis results identifying patient actions that are likelyto be adhered to by this patient, and established rules, patterns,algorithms, logic, etc., for generating personalized patient actions andcombining them in a serial manner to generate a sequence of patientactions and goals that together constitute the patient's side of thepersonalized patient care plan (step 570). Based on the selected patientactions in the personalized patient care plan, corresponding monitoractions for all or a subset of the patient actions are generated usingmonitoring action rules, patterns, algorithms, logic, or the like (step580). The monitoring actions are combined with the patient actions inthe personalized PCP (PPCP) which is then output to the patientsystem(s) and assessor system(s) for implementation and monitoring ofthe PPCP (step 590). The operation then ends.

FIG. 6 is a flowchart outlining an example operation for monitoring apatient's performance with regard to a prescribed personalized patientcare plan in accordance with one illustrative embodiment. As shown inFIG. 6, the operation starts by receiving a PPCP (step 610) from whichmonitor actions are extracted and scheduled by an assessor system (step620). A next monitor action in the schedule of monitor actions withregard to this patient is performed based on the schedule (step 630). Itshould be appreciated that the performance of such monitor actions maybe automated, may be performed by a human, or may be a semi-automaticprocess in which different aspects of the monitor action are performedby an automated system and by a human.

In response to the monitor action being performed, monitor data andpatient feedback information are received (step 640). For example, thismay involve interrogating a health/activity monitoring device associatedwith the patient and receiving the corresponding data as a result. Asanother example, this may involve a human assessor calling the patient,asking the patient some questions about the patient's adherence to thePPCP, and then performing data entry to enter the monitor data andpatient feedback information into the assessor system. In still anotherexample, this may involve the patient logging onto an online system andinputting monitor data into the system which then reports theinformation to the assessor system, e.g., a patient entering blood sugarmeasurement data, weight data, symptom data, or the like. Many differentways of obtaining monitor data and patient feedback data may be utilizeddepending on the desired implementation of the illustrative embodiments.

Based on the monitor data and patient feedback information received, adetermination is made by the assessor system as to whether the patientis adhering to the patient action required in the PPCP (step 650). Ifthe patient action in the PPCP is being adhered to, then a determinationis made as to whether more patient actions in the PPCP to be checked(step 660). If so, the operation returns to step 630. If there are nomore patient actions to be checked, then the operation terminates.

If the patient action is not being adhered to, as may be determined froma comparison of the patient's monitor data and feedback to therequirements of the patient action in the PPCP, then an evaluation ofthe level of adherence is performed (step 670). Adherence feedbackinformation is provided to the PCPCM system (step 680) and adetermination is made as to whether the level of adherence is such thatit warrants an adjustment of the patient actions in the PPCP (step 690).This determination may take into account various factors including thenature and importance of the patient action to the overall goal of thePPCP, e.g., taking medication may be considered much more important thatwalking for 30 minutes a day, a number of times this patient action hasnot been adhered to over a specified period of time, e.g., patient failsto walk for 30 minutes for 3 days in the past 5 days, an amount of thepatient action that was actually achieved, e.g., the patient walked for20 minutes but not 30 minutes, and the like. Based on a determined levelof adherence and the nature and importance of the patient action, theassessor system determines whether an adjustment of the PPCP is needed(step 690).

If an adjustment is needed, then the dynamic plan adjustment operationsof the PCPCM system 410 are initiated by a request from the assessorsystem (step 695). If an adjustment is not needed, then the operationcontinues to step 660 where it is determined whether more patientactions in the PPCP need to be evaluated. If so, the operation returnsto step 630, otherwise the operation terminates.

FIG. 7 is a flowchart outlining an example operation for adjusting apersonalized patient health care plan based on an evaluation of apatient's adherence to a prescribed personalized patient health careplan in accordance with one illustrative embodiment. As shown in FIG. 7,the operation starts by receiving a request to adjust the PPCP for apatient, such as from the assessor system (step 710). The patientactions not adhered to are determined (step 720) and correspondingpatient actions that the patient has adhered to in the past (if any) areidentified (step 730). Corresponding patient actions in similar patientPPCPs that the similar patients have adhered to in the past are alsoidentified (step 740).

Alternative patient actions that the patient is likely to be able toadhere to are selected based on the identification in steps 730 and 740(step 750). The alternative patient actions are balanced with existingpatient actions in the PPCP (step 760). This balancing may compriseadjusting other patient actions based on the alternative patient actionsso as to achieve the same overall goals of the patient care plan, e.g.,adjusting nutrition based patient actions based on changes to exerciseor medication based patient actions.

Based on the modified patient actions, corresponding monitoring actionsfor the modified PPCP are generated (step 770) and a modified PPCP withthe alternative patient actions and monitoring actions is generated(step 780). The modified PPCP is output to the patient system(s) andassessor system(s) (step 790) and the operation terminates.

Thus, the illustrative embodiments provide mechanisms for personalizinga patient care plan for a specific patient's own unique set of lifestylecharacteristics such that the patient care plan is not generallyapplicable to a plurality of patients but is specific for the onepatient. Information from various lifestyle information sources may beused along with patient care plan guidelines, demographic information,medical information, various resources, and the like, to generate apersonalization of a more generic patient care plan that meets thedesired goals for addressing a patient's medical condition. Thepersonalization of the patient care plan may take into considerationpatient actions that are successfully and unsuccessfully performed bythe patient in other patient care plans, and by other similar patientswith regard to their own personalized patient care plans. This may bedone on a historical basis as well. Furthermore, the mechanisms of theillustrative embodiments provide monitoring actions for monitoring thepatient's adherence to the personalized patient care plan and initiationof modifications to the personalized patient care plan when suchadherence meets pre-defined criteria indicative of a need for amodification in the patient care plan.

Generation of Clinical Rules for Application to Patient Information

As noted above, the resources database 418 may comprise rules, logic,equations, and the like that are applied by the one or more of thedemographic and medical data analysis engine 412, lifestyle dataanalysis engine 413, and personalized care plan creation/update engine414 for evaluating patient information from EMR and demographics sources420 and patient lifestyle information sources 427 to perform variousoperations. For example, clinical rules may be applied by thedemographic and medical data analysis engine 412 and lifestyle dataanalysis engine 413 to determine cohort membership of the correspondingpatient and store such information in the cohort database 417. Based onthe classification of the patient into one or more cohorts,corresponding personalized care plan actions, requirements, and thelike, may be associated with the patient to generate a personalized careplan and actions to be performed by an assessor. Moreover, applicationof such rules may be used to perform other actions such as identifyingpatients that represent care opportunities for providing improved careto patients, communicating with patients, identifying patients of amedical practice that may be candidates for assisting medical personnelin achieving care goals, and the like.

The rules may be generated by resource generation engine 490 using amanual, automatic, or semi-automatic operation and corresponding toolsfor performing such operations. Although shown in FIG. 4 as separatefrom the PCPCM system 410, it should be appreciated that the resourcegenerating engine 490 may be integrated in PCPCM system 410 in someillustrative embodiments. In some illustrative embodiments, the resourcegeneration engine 490 may provide user interfaces to users of clientcomputing devices for their use in defining rules, equations, and/orlogic for evaluating patient information. In one illustrativeembodiment, these user interfaces comprise graphical user interfaceswith object oriented representations of rule elements that may bedragged and dropped, user interfaces for selection of rule elements froma pre-determined listing, user interface elements for free-form textentry, and the like.

As noted above, in some illustrative embodiments, automated tools may beused either alone or with manual intervention to generate resources forthe resources database 418, such as clinical rules, equations, and/orlogic to be applied to patient information. These tools may be createdby users utilizing a traditional graphical user interface (GUI), or theymay use cognitive computing concepts to assist the user with creating ormodifying rules. Cognitive computing techniques used to build rulesmight include natural language processing or speech-to-text algorithmsand systems for taking natural language input, parsing the input,extracting key features from the natural language input, associatingthese key features with corresponding concepts and values, andgenerating a structured output that essentially converts thenon-structured natural language input to structured information that maybe used to generate results/responses. One example of a cognitive basedmechanism that may be employed for performing such analysis, extractingfeatures, and generating structured information is the IBM Watson™cognitive system available from International Business Machines (IBM)Corporation of Armonk, N.Y.

For example, natural language processing may evaluate a patient careplan guideline from source 426 that states “Medication A is safe foradult male patients younger than 65 years of age and having type 2diabetes without amputation.” From this information, structured data maybe specified for defining a clinical rule such as the result beingadministering medication A and the corresponding structuredcharacteristics being male, 18-65, type 2 diabetes, no amputation. Thesestructured characteristics may then be automatically combined into arule specifying the characteristics required to be present or notpresent for the corresponding result to be applicable, e.g., thecorresponding action to be applicable, corresponding personalized careplan element to be added to the patient's personalized care plan,categorization of the patient in a particular cohort, or any otherresult that is appropriate for the particular implementation which canbe triggered as a result of the conditions/criteria of the rule beingsatisfied.

The rules themselves may be specified in a structured manner as a set ofconditions/criteria specifying characteristics of the patient that mustbe present (AND requirements), those that may be present (ORrequirements), and/or those that must not be present (AND NOTrequirements) for the corresponding result to be applicable to theparticular patient. The characteristics themselves may take manydifferent forms including demographic information, lifestyleinformation, medical information, source information, and the like. Thecharacteristics may be specified in terms of date ranges, values, datasource identifies, medical codes, combinations of characteristics of thesame or different types, or the like.

The rules may be nested or otherwise configured in a hierarchical mannersuch that if one rule is satisfied by the patient information, this maytrigger additional rules to be applied until a final determination as tothe action, communication, classification into a cohort, or other resultis generated. This configuration sets forth one or more cascading setsof rules such that one rule triggers another rule to be evaluated. Forexample, a first rule may look to a first subset of patient informationto determine if the patient is within a specified age range, is aparticular gender, and has been diagnosed with a particular medicalmalady. If all of these criteria are satisfied, then this may trigger asecond rule that looks to lifestyle information of the patient todetermine where the patient lives and if the patient lives in aspecified geographical area, as well as the patient's amount of physicalactivity as determined from the patient's occupation and hobbies orinterests. This information may be classified and compared to thecriteria of the second rule to determine if the criteria of the secondrule are satisfied, e.g., lives is north America and has a sedentarylifestyle, which would then trigger the corresponding result, e.g., addexercise to the patient care plan, initiate a communication to promote agym membership, or the like.

In accordance with some illustrative embodiments, the rules may becategorized into three main types of rules: demographic rules, medicalcode rules, and lifestyle information rules. Demographic rules specifyone or more conditions or criteria associated with patient demographics,e.g., age, gender, geographical location (e.g., portions of theresidence address), occupation, and the like. Medical code rules specifyone or more conditions or criteria associated with medical codes thatdefine symptoms, diagnoses, treatments, medical procedures, and thelike, associated with the patient. Such medical codes may be specifiedin the medical history of the patient set forth in the patient registryentries for the patient, a current medical record entry, lab results,and the like. Lifestyle information rules specify one or more conditionsor criteria associated with lifestyle patient supplied, environmental,geospatial, establishment, or other lifestyle information as discussedabove. It should be appreciated that rules, of the same or differenttypes, may be chained together to generate complex rule ontologieshaving hierarchical or tree-like structures in which cascading sets ofrules are provided. Moreover, some rules may bridge more than one typesuch that a single rule may look at two or more of demographics, medicalcodes, and lifestyle information.

In some illustrative embodiments, the rules may be generally thought ofas comprising conditions/criteria specified in three differentcategories, i.e. “AND” criteria, “OR” criteria, and “AND NOT” criteria.The “AND” criteria specify one or more criteria, all of which must bepresent in order for the rule to be triggered, where triggering a rulemeans that the criteria of the rule has been satisfied and the result ofthe rule is applicable to the patient information. The “OR” criteriaspecify one or more criteria where at least one of the “OR” criteriamust be present in order for the rule to be triggered. The “AND NOT”criteria specify one or more criteria where none of the “AND NOT”criteria can be present for the rule to be triggered.

In some illustrative embodiments, an “Any X” qualifier may be applied tothe different categories of criteria. What is meant by an “Any X”qualifier is that rather than requiring all of the “AND” criteria to bepresent, none of the “AND NOT” criteria to be present, and at least oneof the “OR” criteria to be present, a number of criteria may bespecified as the “X” value and thus, override the default operation ofthe “AND”, “OR,” and “AND NOT” criteria. Or, alternatively, the X mayspecify a number of instances of the corresponding criteria that must bepresent in the patient information. For example, an X value of 2 may beset for the “AND” criteria meaning that the patient information mustinclude at least 2 of the “AND” criteria or at least two instances ofthe “AND” criteria (where these 2 instances may be for the same “AND”criteria, e.g., 2 instances of a medical code of type 2 diabetes).Moreover, an “Any X” qualifier may be applied to the “OR” criteria thatstates that at least X number of the “OR” criteria must be present inthe patient information, or at least X instances of at least one of the“OR” criteria must be present in the patient information. If therequired number of criteria or instances is not met, then the rule isnot triggered.

Moreover, an “Any X” qualifier associated with the “AND NOT” criteriamay specify that the patient information must have at least X number ofthe “AND NOT” criteria to be eliminated from further consideration astriggering the rule. As a default operation, with the “AND NOT”criteria, if a patient's information indicates that the patient has anyone of the criteria specified as an “AND NOT” criteria, then the patientis eliminated from further consideration for triggering the rule.However, by applying an “Any X” qualifier, this default operation may bemodified to require more than one of the criteria to be present or morethan one instances of one or more of the criteria to be present beforethe patient is disqualified for potentially triggering the rule.

By specifying all three categories of criteria, complex rules aregenerated that may be applied to patient information to identifyspecific types of patients for application of the result of the rules.Furthermore, by specifying a rule ontology of a hierarchical ortree-like arrangement, complex evaluations of patient information may beachieved.

FIG. 8 is an example diagram of an example hierarchical set of rules inaccordance with one illustrative embodiment. As shown in FIG. 8, anested hierarchical arrangement is provided comprising rules 810-830.The triggering of a rule 810, for example, causes a subsequent rule 820to be evaluated to determine if it is triggered, which in turn causes asubsequent rule 830 to be evaluated. Each rule's criteria may beevaluated against patient information in a patient registry and/orobtained from other lifestyle and third party information sources. Inthe example shown in FIG. 8, the rules utilize the AND, OR, and AND NOTformat previously mentioned above with the corresponding examplecriteria being shown in the corresponding boxes of the rule 810-830.

In the depicted example, rule 810 is an example of a demographics rulethat uses criteria directed to the demographics of patient information.Rule 820 is an example of a medical code rule that uses criteria that isdirected to the presence or absence of medical codes within the patientinformation. Rule 830 is an example of a rule that combines bothlifestyle information and medical information in a single rule. Forexample, lifestyle information may indicate that the patient issedentary, has a desk job, is not in a diet program or is in aself-monitored diet program, is or is not a vegan or vegetarian, and thelike. Medical information from the patient's EMRs and the like, mayindicate that the patient has or does not have high blood pressure, aswell as other medical conditions. It should be appreciated that thecriteria specified in the rules may include spatial and or temporalqualifiers as well, although not explicitly shown in FIG. 8. Forexample, a rule's criteria may specify that the patient has medical codeX and also has medical code Y within 1 year of the time the patient wasdiagnosed with medical code X.

As shown in FIG. 8, the result generated from the triggering of rule 810is to evaluate rule 820. Similarly, the result generated from triggeringrule 820 is to evaluate rule 830. If all of the rules 810-830 aretriggered, the final result 840 of rule 830 is performed. This finalresult 840 may comprise some recommendation for treatment, an additionof a personal care plan element to the patient's personal care plan,initiating a communication with the patient or medical personnel,assignment of the patient to a particular patient cohort, or othersuitable operation based on the particular implementation. If any of therules in the hierarchy do not trigger, then the subsequent evaluationsare not performed, i.e. the results of triggering the rule are notfollowed. It should be appreciated that the resources database 418 maycomprise a complex set of rules of these types in various hierarchies ortree-structures for application to patient information.

FIGS. 9A-9C are diagrams illustrating example graphical user interfacesfor rule generation in accordance with one illustrative embodiment. Thegraphical user interfaces (GUIs) shown in FIGS. 9A-9C may be presentedby the resource generation engine 490 to a user via the user's clientcomputing device and one or more data networks and may be used by theuser to define one or more rules for application to patient information,such as patient information in the patient registry.

FIG. 9A illustrates an example GUI for defining various medical codesthat are recognizable by the mechanisms of the illustrative embodimentsand used in the various rules of the illustrative embodiments. In FIG.9A, medical codes indicating a diagnosis of diabetes are entered into alist of codes. In this example GUI, these codes are used by rules toidentify diabetic patients when a matching code is found within thepatient's medical record. Matching codes are then subjected to furtherselection criteria described within the rules engine, as shown in FIG.9C described hereafter.

FIG. 9B illustrates an example GUI for defining a rule for identifyingpatients that are part of a cohort of patients. As shown in FIG. 9B, theGUI comprises a field for specifying a rule name, e.g.,“Diabetes—HEDIS”, and a field for the short name of the rule which maybe used to reference the rule in other rules, where HEDIS refers to theHealthcare Effectiveness Data and Information Set tool. The rulecomprises an AND set of criteria, e.g., patients aged 18-75, an OR setof criteria, e.g., Two ICD/EM combinations or one ICD/EM combination anda DM Problem, and a ANDNOT set of criteria (none shown in the depictedexample). The rule hierarchy is shown in the top left portion of theGUI. Various rules may be generated using this GUI and hierarchicalcombinations of rules may be generated through references to the shortnames of other rules. The hierarchies may be depicted in the top leftportion of the GUI during generation. In this GUI example shown in FIG.9B, the user has selected the rule at the top of the tree. The treeshows the hierarchy of the entire rule, while the lists depicted on theright side of the screen show the details for the top level of thehierarchy. The user can edit the rules and move them around in thehierarchy using the tree or by manipulating items on the detailed viewdisplayed on the right hand side of the screen. Users can use the treeor the detailed view to add/edit/delete rules from the rule hierarchy.Users can also drag/drop or cut/copy/paste individual rules or an entirerule hierarchy from another rule.

FIG. 9C illustrates an example GUI for adding a medical code based rulereferenced by the rule shown in FIG. 9B. In this example, the user hasselected a code rule in the hierarchy “Two ICD9/EM combinations” thatsearches for instances of the list of diabetes codes depicted in FIG.9A. When a user selects the code rule in the tree or double-clicks it inthe detailed view, the detailed view changes to show details about thecode rule. The detailed view shown in FIG. 9C depicts the criteria forthe code based rule. This rule requires a two instances of diabetescodes entered on the same date as an office visit. Applicable officevisit codes are identified within the ENC-27 code table (not shown, butsimilar to FIG. 9A). In this case, the medical code rule GUI comprises atitle field for defining the title of the medical code rule, a codetable field that specifies the medical code table that comprises themedical code information referenced by the medical code rule, e.g., themedical code table data structure defined using the GUI of FIG. 9A.Various tabs and corresponding fields are provided that permit thedefinition of the particular medical codes, combinations of medicalcodes, and requirements associated with these medical codes to satisfythe criteria of the medical code based rule shown in FIG. 9C.

FIG. 9D illustrates an example GUI for assembling rule relationships inaccordance with one illustrative embodiment. In this example, multiplerules are defined as a starting point for another rule. In the exampleshown, only patients that match the criteria specified by both“sDM_A1C_INVERSE” rule AND the “sDM_A1C_VH” rules will be considered.The GUI allows the user to add or remove related rules as needed.Patients that match both of these rules will be matched against criteriafor the “sDM_A1C_UNC” rule, which contains additional code rule andother criteria to identify diabetic patients with uncontrolled HBA1Clevels.

FIG. 9E represents an example rule flow illustrating the application ofa rule in accordance with one illustrative embodiment. This is thehigh-level logical flow for the rules depicted by the GUI shown in FIGS.9A-9D. This diagram also shows how the registry rules are used to flowinto a care plan for a patient.

The resulting rules may be stored in the resources database 418 and usedby one or more of the analysis engines 412, 413 and personalized careplan monitor engine 415 to generate personalized care plans forpatients, initiate communications with patients and/or medicalpersonnel, associate patients with patient cohorts in cohort database417, which may in turn be associated with particular patient care plans,communications, or other actions, or the like. In some illustrativeembodiments, the rules may be used to identify patients that representcare opportunities, where a care opportunity is a patient whosecondition or medical care is not sufficient to manage the patient'shealth or where modifications to the medical care may likely improve thepatient's medical condition or management of their medical condition.The identification of care opportunities may be a basis for initiatingother operations, such as instigating communication with the patient inaccordance with communication workflows, identifying the patient as acandidate for improving medical personnel goal attainment, initiatingthe application of a medical campaign to the patient, or the like.

It should be appreciated that the rules may be stored in the resourcesdatabase 418 in various sets or ontologies directed to performingdifferent types of operations. For example, one set of rules may bedirected to identifying patient cohorts with which patients may beassociated. A second set of rules may be directed to selecting patientsfor which a communication workflow should be applied to try to have thepatient perform a compliance operation, e.g., schedule a doctor'sappointment, submit to a particular test or medical procedure, fill aprescription, take medication, or the like.

In some illustrative embodiments, different sets of rules areestablished for determining whether patients are in compliance withtheir associated patient care plans or that the medical conditions arebelieved to be “under control” due to the patient and medical personnelperforming the necessary actions to manage the medical condition. Forexample, the rules will be evaluated to identify the patient as either“in compliance”, “non-compliant”, or “excluded.” Excluded patients areidentified by a set of exclusion rules that define conditions thatexcuse a patient from normal clinical guidelines. For example, rulesidentifying patients due for a mammogram will include “EXCLUDE” rules inorder to excuse those patients whose medical history includes adual-mastectomy or active breast cancer so that they are consideredcompliant. Those excluded patients may be addressed under other rulesthat address different medical guidance specific to their condition.

A rule, or rules, may specify criteria for considering the patient to bein compliance with their treatment. If the rule, or set of rules,triggers, then the patient is considered compliant. If not, the patientis considered non-compliant, unless some other criteria is met toindicate that the patient is “excluded.” Thus, based on the applicationof rules such as those discussed above, it may be determined thattreatment A, from a set of treatments A-D, is to be associated with thepatient. A separate set of rules may be applied to the patientinformation after treatment A has been prescribed, to determine if thepatient is in compliance with their treatment or not. Those patientsthat are determined to be non-compliant are considered careopportunities for which additional actions may be triggered. Forexample, enrolling the patient in a campaign, performing an outreachoperation by initiating a communication with the patient, generating ormodifying the patient care plan, such as described above, or the like.

Variable Clinical Rules and Caching of Medical Codes for Application toPatient Information

In some illustrative embodiments, the clinical rules (or simply “rules”as used herein) generated may be applicable to a variable set of inputelements obtained from a variety of different information sources. Asnoted above, the patient's EMRs, demographic information, lifestyleinformation, and the like, may be obtained from a variety of sources.EMR information may comprise information from medical service providersabout medical services and procedures performed, medical diagnosisinformation from medical personnel coded in electronic medical records,lab test results, medication information from electronic medicalrecords, pharmacy computing systems, or the like, allergy informationfrom medical records, immunization information, social historyinformation as may be obtained from questionnaires presented by medicalpersonnel (e.g., information about siblings, sexual history, notes abouthome life, abuse, etc.), reconciliation information from medical records(e.g., records of encounters with the patient after a medical service isprovided), medical billing information, insurance claims information,encounter information (e.g., office visits completed), appointmentinformation, medical problem information (e.g., smoker), and the like.The patient information in the EMRs may include patient identifiers,medical codes, event dates, status information, various medical values,birthday information, gender, race, ethnicity, insurance providerinformation, employment information, and the like, which may be compiledfrom various sources. Thus, with a large number of potential sources ofinformation, it can be seen that many instances of information in thesevarious sources may map to a similar variable, e.g., many instances ofmedical codes in various sources may be indicative of a single medicalcondition or medical problem associated with the patient, e.g., patientis a smoker, patient has high blood pressure, etc.

The variable set represents characteristics of the patient, as specifiedin patient information of the patient registry obtained from a varietyof sources, that correspond to a particular variable input to theclinical rules, but which does not in itself eliminate the patient fromfurther consideration as triggering the clinical rule. That is, justbecause a patient characteristic is present in the variable set does notmean that the patient necessarily is considered to have a particularvariable value associated with it, e.g., just because the patient has amedical code in the variable set indicating that the patient is aprevious smoker, does not mean that the patient is an active smoker. Thevariable set may comprise multiple instances of a characteristic, e.g.,multiple instances of a patient's Low Density Lipoprotein (LDL) and/orHigh Density Lipoprotein (HDL) cholesterol values being recorded in thepatient's EMR over time. The variable set may comprise multiplevariables from a plurality of sources that all feed into a singlevariable used by the clinical rules. The variable set, or listing, maybe evaluated to determine the confidence of a value for the ultimatevariable that the listing is associated with. Alternatively, the listingmay be searched for any instance that meets a criteria of a rule suchthat if at least one instance within the listing satisfies a criteria ofthe rule, then that part of the rule is satisfied for the evaluation ofthe patient information.

The variable set or listing (hereafter referred to as a “variablelisting”) operates to cache patient information specific to a particularinput variable for evaluation by the rules. For example, in the case ofa medical code rule, the variable listing may cache or store a list ofeach instance of different medical codes found in various sources thatare associated with the variable with which the variable listing isassociated. As one example, a variable listing may be associated with avariable of “smoker” which indicates whether the patient is a smoker ornot. These medical codes may be of various types depending upon thesource from which they are obtained. For example, a first medical codefrom a first source may indicate a previous smoker, a second medicalcode from a second source may also indicate a previous smoker, a thirdmedical code from a third source may indicate that the patient does notsmoke cigars, a fourth medical code from a fourth source may indicatethat the patient has purchased or used a smoking habit suppressionproduct, such as a gum or patch. The combination of these medical codesprovide information from various sources that together give a picture orevidence as to the proper value for the variable associated with thevariable listing, e.g., whether the patient is likely a smoker or not.

Variable list analysis logic may analyze the variable list to determinewhat the appropriate value is for the variable associated with thevariable list. The various medical codes, patient information, and thelike, are processed using cognitive processes to determine anappropriate value for the variable. For example, if the a variable listis associated with the variable “smoker” and there are 20 instances ofmedical codes or patient information entries present in the variablelist, a simple majority evaluation may be used to indicate whether themajority of instances are indicative of the patient being a smoker. Ofcourse, weighted evaluations may be utilized as well, where instancesfrom certain sources, types of sources (EMRs, lifestyle informationsources, etc.), certain medical codes, and the like, are weighted moreor less heavily. From the analysis, the variable list entries may becollected to evaluate the ultimate variable.

Alternatively, or in addition, the variable list may be maintained as acache of patient information associated with a variable. In this way,when a rule needs to evaluate a patient's information with regard to aparticular variable, the analysis can be performed with regard to thecached information in the variable listing rather than having to accessthe patient information in the patient registry and extract theinformation from the patient registry, which may be time consuming. Forexample, a rule may specify as an “AND NOT” criteria, that the patientis not a smoker, or does not smoke cigars, or is not a previous smoker.As a result, the rule may be applied to cached patient information inthe variable list associated with the variable “smoker” and the cachedpatient information may be analyzed to identify whether any of thecached patient information meets the criteria set forth in the rule oran aggregation of the cached patient information, or a subset of thepatient information, meets the criteria set forth in the rule. If so,then the patient may be eliminated from further consideration. If not,then the patient may be considered as potentially triggering the rule aslong as the other criteria of the rule is met.

In such an embodiment, the variable listing cache of patient informationis progressively and dynamically updated as new patient information isreceived from the various sources. The variable listing cache of patientinformation may be maintained as subsequent rules are applied againstthe patient information. When a rule is to be evaluated, a determinationis first made as to whether a variable listing exists for a variablethat is referenced by the rule. If so, then this variable listing isanalyzed and the criteria of the rule referencing the variable areevaluated against the variable listing. If a variable listing does notexist for the variable, then the standard mechanisms described above foraccessing patient information from the patient registry is utilized.

In some illustrative embodiments, the variable listing stores instancesof patient information that may represent the same type of indication,but as separate instances. For example, if the patient has multiple LDLcholesterol test results provided in their patient information of thepatient registry, and cached in a LDL cholesterol variable listing. Inthis example, each test result value may indicate a value greater than180, which is indicative of a high LDL level. A rule may state thatthere must be at least 2 instances of high LDL and by looking to the LDLcholesterol variable listing and evaluating the values stored in thevariable listing against a high LDL level criterion, the rule mayindicate that the patient information satisfies this portion of therule.

FIG. 10 is an example diagram illustrating a use of a variable listinginput to a clinical rule in accordance with one illustrative embodiment.As shown in FIG. 10, patient information from a patient register 1010 isinput to the PCPCM system 1020, such as PCPCM system 410 in FIG. 4,which analyzes the patient information, such as by way of analysisengines 412 and 413, for example. The analysis extracts patientinformation of interest which may be correlated with one or morevariables 1030 and cached in corresponding variable listing datastructures 1040 associated with the variables. These variable listingdata structures 1040 may be stored in a resources database, such asresources database 418, for example, and indexed by the variable 1030name, e.g., “smoker”, or other variable 1030 identifier for subsequentlookup and retrieval.

When evaluating a patient's information from a patient registry byapplying rules from the resources database, the corresponding variablelisting data structures 1040 for the patient may be retrieved from theresources database and maintained in a memory or other quick accessmemory, such as a cache memory, for quick access. Variable listing datastructures 1040 to which a rule 1050 applies are retrieved based on thevariables referenced in the criteria of the rule 1050. The entries inthe variable listing data structures 1040 may be analyzed to determineif corresponding criteria associated of the rule 1050 are satisfied byone or more of the entries in the corresponding variable listing datastructures 1040 or a value of the variable 1030 generated based onanalysis of the entries in the corresponding variable listing datastructure 1040.

Thus, in addition to the rule structures previously described above,mechanisms are provided for caching patient information for variablessuch that a single variable may have multiple instances of patientinformation relevant to the variable cached in a variable listing forquick retrieval and applicability of rules. These variable listing datastructures may be maintained in a cache for quick access by large setsof rules. In this way, the application of the rules to the patientinformation is made more efficient by increasing the speed by which theapplication of the rules is performed.

Clinical Condition Based Cohort Identification and Evaluation

As mentioned above, the rules and resources of resources database 418 inthe PCPCM system 410 may be used to identify cohorts of patients as wellas initiate various operations such as creating personal care plans,elements of personal care plans, initiate communications, and the like.In one illustrative embodiment, the PCPCM system 410 may be extended toincorporate, or work in conjunction with, a clinical condition basedcohort identification and evaluation system, hereafter simply referredto as a “cohort system.” The cohort system operates to identifydifferent types of cohorts of patients, based on the application ofcohort rules in the resources database 418, as discussed above. Thecohort system identifies groupings, or cohorts, of patients based oncommon combinations of patient information, such as patient registryinformation obtained from the various sources 420-425 describedpreviously. For example, a patient may be analyzed by the cohort system,by applying cohort rules that identify patients that are to beconsidered members of a specified cohort, and determining if the patienttriggers the rules and thus, is a member of the specified cohort.

For example, one or more cohort rules may be established for identifyingpatients as patients that are type 2 diabetes patients. Thus, allpatients in the patient registry that have medical codes or otherpatient information indicating that the patient is a type 2 diabetespatient will be classified in a type 2 diabetes patient cohort.Identifiers of the patients that are members of a cohort may be storedin a cohort data structure in the cohort database 417. It should beappreciated that the granularity of the cohort may be various levels anda patient may be a member of more than one cohort. For example, a firstcohort may comprise type 2 diabetes patients. A second cohort maycomprise type 2 diabetes patients that also have a foot amputation. Athird cohort may comprise type 2 diabetes patients that also have a footamputation, are female, and live in the southern Florida geographicalarea.

In some illustrative embodiments, cohorts are analyzed to identify“meaningful” combinations of patient registry information. A meaningfulcombination of patient registry information may comprise, for example,patient registry information that is common to patients associated witha positive outcome. That is, the cohort system, in addition toidentifying a patient cohort, may comprise analysis mechanisms thatanalyze the patient information to identify successful outcomes, e.g.,patients that kept their doctor appointments, patients that reportedreduction in symptoms, or the like, for patients of a particular type orhaving a particular medical malady, e.g., type 2 diabetes patients, anddetermining combinations of patient profile information that are commonamongst these patients. The definition of a successful outcome isimplementation specific and may be defined by a subject matter expertand provided as a configuration parameter for the analysis mechanisms.In general, a successful outcome is a behavior, action, or activity thatmedical personnel and organizations want patients to engage in so as tomaximize the health of the patient or manage a condition of the patient,or an improvement in a medical condition of the patient. Such successfuloutcomes in most cases bring the patient into compliance with thepatient's prescribed personalized patient care plan or other guidelinesfor treatment of the patient.

A cohort, or “sub-cohort”, of patients within the specified cohort isgenerated centered about common characteristics, common interactions,and the like, of the patients determined to have successful outcomes.The successful outcome cohort, or successful outcome sub-cohort, isgenerated so as to indicate the patient profile information that is mostlikely going to result in the corresponding successful outcome. Thecommon characteristics, interactions, etc., of the patient informationmay specify not only commonalities of the patients themselves, but alsocommonalities of interactions performed by assessors, automatedmechanisms, and the like, to influence the patient so as to achieve thesuccessful output, e.g., communications sent to the patient, personalinteractions with medical personnel or medical condition assessors,assignment of personal care plans, or portions of personal care plans,to the patient, medications prescribed, therapies or treatments appliedto the patient, or the like. Thus, for example, it may be determinedthat within a type 2 diabetes cohort, a sub-cohort that has successfullykept their annual foot exam appointments with their doctor are femalepatients aged 40-50 and that received an email and a follow-up call froma medical assessor to remind the patient to have their annual foot exam.

From the identification of the successful outcome cohorts orsub-cohorts, actions to perform, communications to send, personalizedcare plan or elements of personalized care plans that may be assigned toa patient, or the like may be identified for application to otherpatients that are members of the super-cohort or other cohort with whichthe successful outcome cohort or sub-cohort is associated. For example,if it is determined that female patients aged 40-50 that received anemail and a follow-up call from a medical assessor kept their foot examappointments or made foot exam appointments, then this same email andfollow-up call communication protocol may be applied to other femalepatients in the super-cohort of type 2 diabetes female patients aged40-50 that may live in other areas other than southern Florida, or othertype 2 diabetes patients in general.

FIG. 11 is an example block diagram of primary operational elements foridentifying successful outcome cohorts in accordance with oneillustrative embodiment. As shown in FIG. 11, the primary operationalelements include a resources database 1110 comprising, among otherresources, one or more sets of cohort rules 1112 for use in definingvarious cohorts of patients of interest based on the patient informationobtained from a patient registry 1120 comprising electronic medicalrecords 1122, demographic information 1124, and lifestyle information1126 for a plurality of patients. The resources database 1110 may besimilar to resources database 418 in FIG. 4, for example. Moreover, thepatient registry 1120 may comprise patient information obtained from thevarious patient information sources 420-425 in FIG. 4.

A cohort system 1130 is provided that includes patient cohortclassification engine 1132 and successful outcome cohort analysis engine1134, among other logic not explicitly shown. Any operations describedas being performed by the cohort system 1130 that are not directlyattributed to either the patient cohort classification engine 1132 orsuccessful outcome cohort analysis engine 1134 may be performed by otherlogic provided in the cohort system 1130. The patient cohortclassification engine 1132 applies cohort rules 1112 to patientinformation 1128 obtained from the patient register 1120 to identify oneor more cohorts 1140 and the particular patients whose patientinformation 1128 indicates that the patient is a member of the one ormore cohorts 1140. The identified cohorts 1140 and their membershipidentifiers, i.e. identifiers of patients (e.g., patient IDs) that aremembers of the respective cohorts, are stored in cohort data structuresin the cohort database 1150. The cohort database 1150 may be similar tocohort database 417 in FIG. 4, for example.

The successful outcome cohort engine 1134 analyzes patient informationfor patients that are members of cohorts 1140 to identify successfuloutcome cohorts 1160, i.e. cohorts or sub-cohorts of patients that havehad successful outcomes as defined by configuration information definingsuccessful outcomes as provided to the successful outcome cohort engine1134. Configuration information used to configure the successful outcomecohort engine 1134 may identify successful outcomes for various types ofpatient cohorts 1140. Thus, for example, for a cohort associated withtype 2 diabetes patients, a successful outcome may be defined as thepatient completing their annual foot exam. For another cohort,associated with bladder cancer patients, a successful outcome may bedefined as scheduling their 6 month follow-up physician appointments.Various types of successful outcomes may be pre-defined and used toconfigure the successful outcome cohort engine 1134. The configurationinformation may be used to analyze the patient information for specifiedcorresponding cohorts 1140 retrieved from the cohort database 1150 todetermine which patients in each of the cohorts are determined to havehad successful outcomes and define a successful outcome cohort, orsub-cohort.

Thus, for example, the successful outcome cohort engine 1134 mayretrieve a cohort 1140 for type 2 diabetes patients. The successfuloutcome cohort engine 1134 may then analyze the patient information 1128retrieved from patient registry 1120 for each of the patients that aremembers of the type 2 diabetes patients cohort 1140. For those that havepatient information that indicates a successful outcome of completingtheir annual foot exam, these patients are associated with anothercohort referred to as a successful outcome cohort 1160. In a sense, thissuccessful outcome cohort 1160 is a sub-cohort that is comprised of asubset of the members of the type 2 diabetes cohort 1140.

The successful outcome cohort(s) 1160 may be provided to a medical careintervention analysis system 1170. The medical care interventionanalysis system 1170, while shown as a separate system from cohortsystem 1130, may be integrated with cohort system 1130 in someillustrative embodiments. Moreover, the medical care interventionanalysis system 1170 may be part of an extension of the PCPCM system 410in FIG. 4 in some illustrative embodiments. The medical careintervention analysis system 1170 analyzes the patient information forthe patients that are part of the same successful outcome cohort 1160 toidentify commonalities amongst the characteristics in the patientinformation. For example, commonalities may include demographicinformation, actions performed, communications sent to the patients,temporal characteristics of communications/actions performed, and thelike. A measure of prevalence of the commonalities is calculated foreach of the commonalities identified and the prevalence measures may becompared to one or more threshold values to select commonalities thatshould be used as a basis for recommending interventions for otherpatients having similar characteristics.

For example, the medical care intervention analysis system 1170 mayanalyze patient information for patients of a successful outcome cohort1160 associated with type 2 diabetes patients. It may be determined that35% of these patients were contacted by electronic mail, using aspecified script or content form X, and then a follow-up automated phonecall was made 3 days later using a specified script Y. It may also bedetermined that 10% of these patients were contacted only by theautomated phone call, but using a script Z. Another 25% may havereceived only the electronic mail message using script or content formX. Still a further 25% scheduled their foot exam at their previousdoctor's visit and no follow-up was required. Another 5% may not be ableto be identified as having any discernable commonality of communicationssent to them. Moreover, other commonalities, such as geographicalregion, gender, age, etc., may be evaluated and used to identifyclassifications of patients.

Assuming a threshold value of 30%, in this example, the onlycommunication option that meets the threshold criteria as indicative ofan influential intervention with the patient that assisted in helpingthe patient to achieve a successful outcome is the communicationprotocol comprising the electronic mail message with script Xfollowed-up by an automated phone call 3 days later using the specifiedscript Y. This information may be associated with the cohort 1140 thatis the original basis for the creation of the successful outcome cohort1160 and/or the successful outcome cohort 1160 itself. This informationmay further be provided to one or more intervention systems 180, such asthe PCPCM system 410 in FIG. 4, a communications system, a campaignsystem, such as an advertising campaign, or the like, to initiate or atleast recommend the corresponding intervention actions deemed to becommon to the successful outcome cohort 1160 members at thepredetermined threshold value level or higher.

Using this information, the PCPCM system 410 or other logic may analyzepatient information from the patient registry 1120 to identify patientsthat are not in compliance with their associated personalized patientcare plans. For example, the personalized care plan monitor engine 415may analyze the patient information to identify whether a patient is incompliance or not. If the patient is not in compliance, then the patientis considered to represent a care opportunity, or “care op.” Such careop patients may be classified into one or more cohorts 1140 and thecorresponding successful cohort intervention actions may be recommendedfor assisting the patient in coming into compliance with theirpersonalized care plan. The patient identifier for the patient that isnot in compliance as well as information about the recommendedintervention operation to be executed to bring the patient intocompliance, may be sent to the one or more intervention systems 180 tocause corresponding intervention operations, e.g., phone calls,electronic mail messages, text messages, modifications to personalizedcare plans, initiating of a campaign, or the like, to be performed.

It should be appreciated that the identification of the interventionoperation to be performed may be based on the nature of thenon-compliance of the patient. For example, if the patient has failed toschedule an appointment with the doctor, then a first interventionoperation associated with patients who successfully scheduled anappointment with their doctor, and have similar characteristics to thepatient that is determined to be non-compliant, may be selected for use.If the patient has failed to obtain a test from a lab, then a secondintervention operation associated with patients that successfully hadthe test performed at the lab, and have similar characteristics to thenon-compliant patient, may be selected for use. Thus, the particularintervention action is based on the type of non-compliance, thesimilarity of characteristics of the non-compliant patient with thecharacteristics of patients that had a successful outcome, and thecommonality of the characteristics of the patients that had a successfuloutcome.

FIG. 12 is a flowchart outlining an example operation for identifyingsuccessful outcome cohorts in accordance with one illustrativeembodiment. As shown in FIG. 12, the operation starts by receivingpatient information from a patient registry (step 1210) and applyingcohort rules to the patient information to generate one or more cohortsof patients (step 1220). The patient cohorts are analyzed to identifysuccessful outcome sub-cohorts for each of the one or more cohorts basedon configured information indicating what a successful outcome is in thecontext of the cohort (step 1230). It should be appreciated that theremay be multiple successful outcome sub-cohorts for each cohort based onthe particular successful outcome configuration information, e.g., forthe same cohort, there may be multiple different outcomes considered tobe successful such as scheduling a doctor appointment, getting aparticular test performed, reducing LDL cholesterol levels by a certainamount, etc.

The successful outcome sub-cohorts are analyzed to identifycommonalities between characteristics in the patient information for thepatients that are members of the successful outcome sub-cohorts (step1240). A measure of prevalence of the common characteristics among themembers of the successful outcome sub-cohort is generated and comparedto a threshold value (step 1250). Characteristics whose prevalence meetsor exceeds the threshold value are selected as representative of themembers of the successful outcome sub-cohort (step 1260) andcorresponding intervention actions are identified in thesecharacteristics (step 1270). The intervention actions are stored inassociation with the cohort and/or successful outcome sub-cohort forlater use in recommending intervention actions for non-compliantpatients having similar characteristics (step 1280). The operation thenterminates.

FIG. 13 is a flowchart outlining an example operation for applying anintervention action to a non-compliant patient in accordance withpreviously identified successful outcome sub-cohorts in accordance withone illustrative embodiment. As shown in FIG. 13, the operation startsby monitoring performance of a patient's personal care plan to determinecompliance (or adherence) or non-compliance (or non-adherence) (step1310). This may be done in a similar manner as described above withregard to FIG. 6, for example. If the patient is determined to be incompliance (step 1315), then the operation terminates. If the patient isdetermined to not be in compliance, then the patient's information isanalyzed and cohort rules are applied to classify the patient into apatient cohort (step 1320).

The characteristics of the patient information are matched to asuccessful outcome sub-cohort associated with the patient cohort (step1330). This matching may be based on the particular type ofnon-compliance identified. Moreover, this matching may be based oncharacteristics of the patient, e.g., gender, age, geographicallocation, etc. For example, the patient may be determined to benon-compliant with regard to scheduling their annual foot exam for type2 diabetes patients. Moreover, the patient may be determined to be a 45year old female. The patient cohort may be a cohort for type 2 diabetespatients having various successful outcome sub-cohorts, one of which isfor patients that successfully scheduled their annual foot exam, thatare female, and are aged 40-50. Based on the patient's non-complianceand personal characteristics, the patient is matched to the sub-cohortfor patients that successfully scheduled their annual foot exam, arefemale, and are aged 40-50.

A corresponding intervention action associated with the successfuloutcome sub-cohort is selected (step 1340). As noted above, thisintervention action may take many different forms depending on theimplementation. In some illustrative embodiments, these interventionactions may be classified into types of campaigns, types of outreach orcommunication with the patient, and types of care plans or care planelements. As will be described hereafter, in some illustrativeembodiments, this intervention action may comprise one or morecommunications and/or a sequence of communications, selected based onthe identification of the communications and/or sequence ofcommunications that was determined to be associated with patients thatare members of a corresponding successful outcome sub-cohort.

The intervention action and an identifier of the patient are provided toan intervention system (step 1350). The intervention system initiatesthe identified intervention action with regard to the particular patient(step 1360) and then the operation terminates. For example, theintervention system may comprise an automated electronic mail systemthat automatically generates electronic mail messages and sends them toelectronic mail addresses associated with patients. The electronic mailmessages may have predefined scripts or templates which are populatedwith patient information for the patient. As another example, automatedtelephone call systems may be utilized that automatically make telephonecalls to telephone numbers associated with patients and play apre-recorded script, possibly integrated with some patient informationsuch as a name, physician name, etc. that is audibly output using atext-to-speech type mechanism. In some cases, the intervention systemmay be a human operator that manually performs actions in accordancewith the intervention action. The intervention system may be separatefrom the other elements of the invention and in fact may be provided bya third party vendor in some cases.

Thus, in addition to the other mechanisms described above for generatingand monitoring personal care plans for patients, the mechanisms of theillustrative embodiments may further apply rules to patient informationto identify patient cohorts. The mechanisms of the illustrativeembodiments may further identify, within these patient cohorts, othercohorts, or sub-cohorts, that are associated with patients that have hadsuccessful outcomes. The patient information for these patients havingsuccessful outcomes may be analyzed to identify commonalities that mayhave lead to the successful outcome. Corresponding intervention actionsmay be identified and associated with the cohort and/or sub-cohort.These intervention actions may then be recommended, or automaticallyinitiate, in response to a non-compliant patient being identified andcorrelating the patient information of the non-compliant patient withthe commonalities of the patients having had successful outcomes. Inthis way, intervention actions are performed to increase the likelihoodthat the patient will come into compliance with their personalized careplan or other health management guidelines.

Communication with Patients Based on Historical Analysis

As mentioned above, the illustrative embodiments provide mechanisms forgenerating personalized patient care plans, monitoring a patient'sadherence or compliance with a personalized patient care plan, anddetermining appropriate intervention actions to take to increase thelikelihood that the patient will become compliant or adhere to thepersonalized patient care plan if the patient becomes non-compliant ordoes not adhere to the patient care plan ascribed to them. Indetermining appropriate intervention actions to perform, if theintervention action involves a communication with the patient, whichmost often it will, it is desirable to communicate with the patient in amode that is most likely going to result in the patient performing acompliance action or event so as to generate a successful outcome. Acompliance action or event is one that brings the patient intocompliance with the prescribed personalized patient care plan, or ingreater compliance if not complete compliance with the prescribedpersonalized patient care plan.

As noted above, one way to identify this best mode of communication isto look at a successful outcome cohort with which the patient hassimilar characteristics, to identify the intervention actions (e.g.,communications) associated with the successful outcome cohort, e.g.,female patients aged 40-50 that are type 2 diabetics that havesuccessfully completed their annual foot exam were contacted byelectronic mail 30 days before their annual due date for their exam,with a follow-up automated phone call 3 days later. This information maybe used either alone or in combination with other analysis of thepatient's personal communication history, specified consents orpreferences for communication, and other pattern analysis of patientshaving similar characteristics to that of a patient in question, toidentify the best mode, or sequence of modes, for communicating with thepatient that have a highest probability or likelihood of causing thepatient to engage in a compliance action or event.

In some illustrative embodiments, the mechanisms of the illustrativeembodiments analyze the patient's personal patient information toidentify instances in the patient's history where communications weremade to the patient which resulted in a subsequent compliance action orevent, e.g., an email reminder message sent to the patient followed bythe patient scheduling an appointment within a predetermined period oftime from the date/time of the email reminder message. These instancesmay indicate particular communication types or particular communicationworkflows, as described hereafter. Instances where communications weremade that did not result in a compliance action or event may also beidentified as well. A measure may be calculated for each communicationtype, combination of communication types, communication workflows, orthe like, with regard to how often the communication(s) or workflowresulted in a subsequent compliance action or event within apredetermined period of time, indicative of the subsequent complianceaction or event being attributable to the corresponding communication(s)or workflow. Positive instances (where a compliance action/eventoccurred within the predetermined time period) may increase this measurewhile negative instances (where a compliance action/event did not occurwithin the predetermined time period) may decrease this measure.Moreover, the type of compliance action or event may be identified withregard to each measure so as to identify which communication type(s) orworkflows worked best for this particular patient for influencing thepatient to perform a particular compliance action or event. That is,these measures may be compared to each other to determine whichcommunication(s) are best for which types of compliance actions/eventsdesired.

In addition, this information may be correlated with specificpreferences and consents specified in the patient information. Forexample, only communication modes (or types) for which the patient hasgiven a consent to be communicated with may be considered. For thosecommunication modes, the corresponding best modes of communication asdetermined from the patient's history of communications in the patientinformation may be selected. For example, if, for a particular type ofcompliance action/event desired, the best modes of communication basedon the patient's history indicate electronic mail and text messaging,but the patient has only given consent to be contacted by electronicmail, then only electronic mail may be utilized even if text messagingis determined to be the relative better mode to result in the complianceaction/event. Such selection may further be based on the patient'sspecified preferences. Thus, if the patient, in the above example,consented to both electronic mail messaging and text messaging, but hasindicated a preference for text messaging, then text messaging may beselected for use in communicating with the patient to elicit thecompliance action/event.

It should be appreciated that the identification of communicationmode(s) to utilize for eliciting a compliance action/event from apatient may comprise identifying a sequence or pattern of communicationmode(s) as well as timing of communication mode(s) and content ofcommunications. These may be specified in communication workflows asdiscussed below, or as sequences or patterns of communication mode(s)occurring prior to a compliance action/event occurring within aspecified time period of a communication. Thus, for example, a patient'shistory in the patient information may indicate that the patientreceived an electronic mail message followed by a text message 3 dayslater, and followed by a phone call 2 days after the text message. Thispattern or sequence of communication mode(s) may be identified in thepatient information and used as a basis for potential selection for usein eliciting a compliance action or event from the patient in a currentor future situation where the patient is determined to be non-compliant.

In some illustrative embodiments, the identification of the bestcommunication mode(s) to use to elicit a desired compliance action/eventmay be performed in the aggregate over a plurality of patients havingsimilar characteristics. That is, as noted above, rules may be appliedto the patient's information to categorize the patient into a patientcohort with other patients having similar characteristics, e.g.,demographics, medical codes, lifestyle information, and/or the like. Thepatients in a same cohort as the non-compliant patient may have theirpatient information analyzed, in a similar manner to that of thenon-compliant patient, to identify instances of communications, orsequences/patterns of communications, that have a correspondingcompliance action/event within a predetermined time period of thecommunication(s). Again, measures may be calculated for each of thecommunications, or patterns/sequences of communications, and for eachtype of compliance action/event. These measures may be aggregated acrossall of the patients in the same cohort and a selected communication, orsequence/pattern of communications, may be selected for each type ofcompliance action/event for the cohort. This information may then beused to recommend communication mode(s) to be used with thenon-compliant patient to elicit the desired compliance action/event.

It should be appreciated that in some cases, such analysis of similarpatients in the same cohort may be performed with regard to successfuloutcome cohorts, or sub-cohorts, as opposed to the entire originalcohort. That is, since these patients have already been determined togenerate successful outcomes, they are more likely to indicate the bestcommunication mode(s) for other patients having similar characteristics.Moreover, the selection of a successful outcome cohort to use may bebased on the desired compliance action/event, e.g., if the desiredcompliance action/event is the patient scheduling their annual footexam, then a successful outcome cohort associated with patients thathave successfully scheduled their annual foot exam may be used as abasis for identifying the best communication mode(s) to utilize wheninteracting with a non-compliant patient having similar characteristics.

For example, the non-compliant patient may be classified into a cohortfor type 2 diabetes patients that are female, aged 40-50. From withinthis cohort, a successful outcome cohort is defined that represents type2 diabetes patients that are female, aged 40-50, and that have completedtheir annual foot exam, which it is assumed is the complianceaction/event sought from the non-compliant patient. The non-complaintpatient's information is analyzed as well as the patient information forpatients in the successful outcome cohort to identify the bestcommunication mode(s) identified in the non-compliant patient'sinformation and those of similar patients in the successful outcomecohort, such as in the manner previously described above. The bestcommunication mode(s) selected for the non-compliant patient and for thesuccessful outcome cohort may be compared through a weighted comparisonand a selection of a communication mode, or sequence/pattern ofcommunication modes, is made. The weighting may be implementationspecific. For example, in one implementation, a higher weight may begiven to the communication mode(s) associated with the non-compliantpatient's information as opposed to the successful outcome cohortpatients, or vice versa. This weight may be applied to the correspondingmeasures for the communication modes, or sequence/pattern ofcommunication modes, to generate a weighted success value for each ofthe communication modes, of sequence/pattern of communication modes, anda highest weighted success value communication mode or sequence/patternis selected.

In addition to the communication modes, the illustrative embodiments mayidentify the more/less successful communication content for theindividual non-compliant patient and/or aggregate set of patients havingsimilar characteristics to the non-compliant patient. For example, inembodiments where the communications utilize pre-defined templates orscripts, identifiers of the templates or scripts may be maintained inthe patient information along with other communication mode information.These identifiers may be used in a similar manner to the identifiers ofthe communication modes to identify which templates/scripts, and thus,content, of communications are more/less successful in eliciting acompliance action/event from the patient. The templates/scripts may beassociated, such as via metadata associated with the template/script,with different types of personality type or emotional characteristicswhich may be used to identify the types of content that are more/lesssuccessful with the particular patient or group of patients. Forexample, the word choice in a template/script may be considered“forceful” or “friendly” or “urgent” and the patient's historyinformation may indicate that the patient does not respond well to“forceful” communications but responds more readily to “friendly”communications. Therefore, if one wishes to elicit a complianceaction/event from the patient, it is more likely to occur if a friendlycontent communication is utilized. This information may be combined withthe selected communication mode(s) to determine a best mode and contentfor the communication(s) to bring the non-compliant patient intocompliance.

It should be appreciated that the evaluation of content ofcommunications is not limited to implementations where templates/scriptsare utilized. To the contrary, natural language processing of thecontent of previous communications may be performed and keywords or keyphrases may be extracted and correlated with emotional or personalitycharacteristics so as to generate metadata associated with thecommunication instance. This information may be included in the patientinformation in association with the communication instance informationso as to provide an indicator of content in the communication which canbe analyzed in the manner discussed above.

Thus, illustrative embodiments are provided that include mechanisms foridentifying one or more communication modes for communicating with apatient based on individual non-compliant patient information and/oraggregate analysis over a plurality of patients having similarcharacteristics, with regard to a history of responsiveness to differentmodes of communication. In addition to the patient information alreadydiscussed above, the patient information for a particular patient mayinclude communication log data structures for tracking communicationsinitiated with the patient including dates/times, types ofcommunications, identifiers of particular scripts or templates used forthe communications, content metadata, initiators of the communications,whether the communication resulted in a successful contact with thepatient or not, and whether a subsequent compliance action or eventoccurred. The communication log data structures may be analyzed by themechanisms of the illustrative embodiments to identify which modes ofcommunication, and which types of content, are more successful with theparticular non-compliant patient than others. This information may beused along with other patient information for the patient, such ascommunication mode preferences, communication mode consents, and thelike, to select a communication mode that is most likely going to resultin a compliance action or event. In some cases, while the patient mayprefer one mode of communication, if the communication log datastructures indicate that the patient is non-responsive to that mode ofcommunication, an alternative mode of communication may be selectedbased on the communication log data structures, or “communication logs.”As mentioned above, this analysis may be done in the aggregate over aplurality of patients having similar characteristics to thenon-compliant patient, such as patients in a same cohort as thenon-compliant patient or in a successful outcome cohort associated withthe cohort in which the non-compliant patient is classified.

In some illustrative embodiments, the communication logs may storeidentifiers of communication workflows. A communication workflow is aset of one or more communications of the same or different communicationmodes and/or content, temporal information about the one or morecommunications, and content information specifying the type of contentof the particular one or more communications, e.g., metadata indicatingpersonality or emotional characteristics of the content. For example, acommunication workflow may comprise a first email having contentmatching template A being sent at 7 pm on a Wednesday, an automatedtelephone call using script Q being made at 6 pm five days later, and atext message being sent two days later at 3 pm using a template Z.Communication workflows may be pre-defined and associated with anidentifier. Thus, for example, there may be 10 different communicationworkflows with identifiers 1-10 and the communication logs may storethis identifier in the communication log for a patient as part of thepatient's information. The communication workflows may be associatedwith success/failure indicators indicating whether the communicationworkflow resulted in a responsive communication from the patient, e.g.,the patient picking up the telephone, the patient replying to anelectronic mail message, a read receipt from the patient being received,the patient clicking on a hyperlink in the communication or a graphicaluser interface element, or any other suitable indication that thepatient received the communication. This information may also becorrelated with patient information indicating the patient's actionsthereafter with regard to scheduling an appointment with theirphysician, obtaining a lab test, filling a prescription for medicationat a pharmacy, or any other compliance action or event.

It should be appreciated that the communication workflows identifycombinations of communication modes and a temporal aspect ofcommunication regarding an ordering of communication modes, a timing ofcommunications, a number of times each communication mode is to beutilized, and the like. In some cases, the patient information may notspecify an identifier of a specific pre-defined communication workflow.In such cases, sequence or patterns of communications modes and contentmay be identified through pattern analysis of the patient information.Thus, for example, an instance of an email at one date and time,followed by a phone call 2 days later, followed by a text message 3 dayslater, followed by a doctor's appointment being scheduled 30 days laterrepresents a pattern of communications. Logic may be provided foridentifying such patterns in patient information using temporalboundaries, e.g., communications that are within specific time frames ofone another, triggering actions/events such as the complianceaction/event, and the like, as basis for identifying these patterns.Hence, an ad hoc sequence or pattern identifier is provided that canidentify sequences/patterns of communications without pre-definedcommunication workflows. These ad hoc sequences/patterns may be usedeither alone or in combination with communication workflow identifiersto select a best communication mode or sequence/pattern of communicationmodes to use to elicit the compliance action/event from thenon-compliant patient. Thus, the illustrative embodiments may perform acomplex evaluation to determine what particular combinations ofcommunication modes, and what temporal sequence, to utilize forparticular types of patients based on historical analysis of bothindividual non-compliant patient and the aggregate of similar patients.

FIG. 14 is an example block diagram of the primary operational elementsfor selecting an optimum or best communication mode or sequence/patternof communication modes in accordance with one illustrative embodiment.The example shown in FIG. 14 assumes a communication workflow basedimplementation for purposes of illustration. However, as noted above,the illustrative embodiments do not require pre-defined communicationworkflows to be utilized and may in fact operate on ad hoc communicationsequences/patterns identified in patient information based on patternanalysis or the like. FIG. 14 is only intended to be one exampleimplementation and those of ordinary skill in the art will recognizethat many modifications may be made to the depicted example withoutdeparting from the spirit and scope of the present invention.

As shown in FIG. 14, the primary operational elements comprise the PCPCMsystem 1410, a communication workflow engine 1420, a patient registry1430, a cohort system 1440 (shown as a separate system but as notedabove, may be integrated with the PCPCM system 1410 depending on theimplementation), a communications engine 1450, and patient communicationsystems 1470. The PCPCM 1410, patient registry 1430, and cohort system1440 all operates in the manner previously described above. In addition,these elements interface with the communication workflow engine 1420 tofacilitate operations for selecting one or more communication modes forcontacting a non-compliant patient to elicit a compliance action/event.In particular, the PCPCM system 1410 provides information regardingnon-compliant patients to the communication workflow engine 1420, thecohort system 1440 provides information regarding cohort associationwith regard to a non-compliant patient, and the patient registry 1430provides patient information 1432 and communication logs 1434 for thenon-compliant patient and other patients, such as those in an associatedcohort, to the communication workflow engine 1420 for use in selecting acommunication workflow to use to communicate with the non-compliantpatient.

Moreover, the communication workflow engine 1420 interfaces withcommunication engine 1450 to facilitate the sending of communications,in accordance with a selected communication workflow, to patientcommunication systems 1470 and receive responses back from the patientsystems 1470. In addition, the communication engine 1450 providesinformation back to the communication workflow engine 1420 to updatecommunication logs 1434 associated with patient information 1432 in thepatient register 1430.

In operation, the PCPCM system 1410 may identify a non-compliantpatient, e.g., a patient that is not following their prescribed patientcare plan, has failed to keep an appointment, or any other event thatcauses the patient to not be in compliance with prescribed treatmentsfor curing or managing their medical condition. The PCPCM system 1410may send a request message to the communication workflow engine 1420indicating the identifier of the non-compliant patient and the nature ofthe failure to comply, e.g., an identifier of the type of complianceaction/event that is desired from the non-compliant patient. Thecommunication workflow engine 1420 sends a request for patientinformation 1432 and communication log information 1434 to the registry1430 as well as a request for cohort information from the cohort system1440. The patient registry 1430 provides the patient information 1432and communication logs 1434. The cohort system 1440 provides informationas to the cohorts associated with the non-compliant patient identifiedin the request message from the PCPCM system 1410. The communicationworkflow engine 1420 sends a request to the patient registry 1430 forpatient information 1432 and communication logs 1434 for patients in thecohorts associated with the non-compliant patient.

The communication log analysis engine 1422 of the communication workflowengine 1420 analyzes the communication logs 1434 of the non-compliantpatient and the patients in the associated cohorts to identify instancesof communication workflows and their associated success/failureconditions with regard to the particular type of compliance action/eventdesired as specified in the request message from the PCPCM system 1410.Measures of success/failure of the various communication workflows arecalculated, possibly weighting different success/failure values fordifferent communication workflows depending on the implementation. Forexample, weights may be applied based on preferences, consents, or otherinformation in the non-compliant patient's patient information to prefersome communication modes and/or communication workflows over others. Insome cases, greater weight is given to communications orsequences/patterns that are more recent as opposed to those that aremore temporally remote from the present date/time. Other weightingschemes may likewise be used, such as default weights set according tosubject matter expert determinations, or the like.

The workflow selection engine 1424 selects a communication workflowbased on the calculated success/failure measures, retrieves thecorresponding communication workflow from the workflows database 1428,and provides the selected communication workflow to the communicationsengine 1450. The selected communication workflow is used by thecommunication engine 1450 to retrieve corresponding templates/scriptsfor the communications in the communication workflow from thetemplates/script database 1455. The retrieved templates/scripts areoptionally customized based on patient information 1432 for thenon-compliant patient to generate one or more communications to be sentto patient communication systems 1470 associated with the non-compliantpatient using communication information (telephone numbers, emailaddresses, text message identifiers, etc.) specified in the patient'sinformation 1432. These communications are sent using the particularcommunication mode(s) specified in the selected communication workflowat the specified times indicated in the selected communication workflow.Alternatively, these communications may be performed by third partycommunication providers 1460, such as companies that specialized inlarge scale automated calls, electronic mail distributions, textmessaging, or the like.

The communication engine 1450 monitors the communications for resultsand communication log update building logic 1452 builds a communicationlog update based on the results. For example, the monitoring of thecommunications may indicate whether the results of the communicationswith the patient communication systems 1470 may indicate a hang-up onthe call, busy signal, answering machine pick-up, auto-responder emailresponse, email delivery failure, read receipt received, response text,user selecting a graphical user interface element (such as a virtualbutton or the like), or any other response that can be provided inresponse to a particular type of communication. This information may beadded to a communication log update data structure that is provided backto the communication workflow engine 1420 in response to the workflowbeing completed.

The workflow results analysis engine 1426 analyzes the communication logupdate data structure to identify communication log updates to beapplied to the communication logs 1434 of the non-compliant patient. Forexample, the workflow results analysis engine 1426 may analyze thevarious responses captured by the communication log update builder logic1452 and determines whether these represent success/failure of theparticular communication mode, communication content, and/orsequence/pattern of communications. The communication logs 1434 are thenupdated with the identifier of the communication workflow utilized, thedate/time of the communication workflow selection and/or completion, andthe success/failure of the communication workflow. Of course, this couldalso be done on an individual communication mode basis as well. In thisway, the communication logs 1434 of the non-compliant patient areupdated to reflect the most recent communication workflow attempt andthereby influence future communication workflow selections.

The PCPCM system 1410 may continue to monitor the patient registry 1420to determine if updates to the patient information 1432 indicate acompliance action/event occurring within a specified time period of themost recent communication workflow selection/completion. The time periodmay be a predetermined time period which is a default or which hisspecific to the type of compliance action/event. If such a complianceaction/event is identified, then the patient may be evaluated to be incompliance. If such a compliance action/event is not identified, thenthe patient may continue to be considered non-compliant and theoperation to select another communication workflow may be performedagain to again try to attempt to bring the patient into compliance.

FIG. 15 is a flowchart outlining an example operation for selecting abest mode or sequence/pattern of communication modes in accordance withone illustrative embodiment. The operation outlined in FIG. 15 may beperformed, for example, by a communication workflow engine, such ascommunication workflow engine 1420 in FIG. 14, for example.

The communication workflow engine receives a request message identifyinga non-compliant patient and a desired compliance action/event (step1510). The communication workflow engine sends requests for patientinformation and cohort information to the patient registry and cohortsystems (step 1512). The communication workflow engine receives thecohort information and patient information/communication logs for thenon-compliant patient (step 1514) and sends a request for patientinformation and communication log information for patients in theidentified cohorts (step 1516).

The communication log information is analyzed to identify instances ofcommunications of sequences/patterns of communications associated withthe compliance action/event sought as specified in the request message(step 1518). Weighted success/failure measures are calculated for thevarious communications or sequence/patterns of communications (step1520). A communication or sequence/pattern of communications is selectedbased on the weighted success/failure measures (step 1522). The selectedcommunication(s) are then used as a basis for initiating communicationswith the non-compliant patient (step 1524). Correspondingtemplates/scripts are retrieved from a template/script database (step1526) and optionally customized based on patient information for thenon-compliant patient to generate one or more communications to be sentto communication systems associated with the non-compliant patient usingcommunication information (telephone numbers, email addresses, textmessage identifiers, etc.) specified in the patient's information (step1528).

The communications sent as part of the selected communication(s) aremonitored to determine if the patient responds to the communications(step 1530). A communication log update is built based on the monitoringof the responses, if any, to indicate the success/failure of thecommunications (step 1532). This communication log update is analyzed togenerate an update to the communication log for the non-compliantpatient (step 1534) and a response is sent back to the PCPCM systemindicating whether the patient responded or not (step 1536). Theoperation then terminates.

It should be appreciated that the PCPCM system may further monitor thepatient information in the patient register 1430 to determine if thereis any subsequent compliance action/event performed by the non-compliantpatient to determine if the patient has come into compliance. If so, thepatient may be re-classified as being a compliant patient. If not, thenthe process above may be repeated as the patient is still non-compliant.However, since the communication logs have been updated, the measures ofsuccess/failure may be adjusted so as to be less likely to select thesame modes of communication or sequence/pattern of communications,communication workflow, and/or content of communications for subsequentcommunication operations.

Thus, the illustrative embodiments further provide mechanisms forselecting communication modes, sequences or patterns of communicationmodes, and communication content for communicating with non-compliantpatients to attempt to bring them in compliance with their personalizedpatient care plans, treatments, or the like. The illustrativeembodiments may look at the communication history of the non-compliantpatient and patients having similar characteristics. Moreover, weightedcalculations of success/failure of previous communications may be usedto calculated values for selection of communication modes to be used.Furthermore, the selection may be based on the particular type ofcompliance action/event desired to be elicited from the non-compliantpatient.

Interpreting Clinical Values in Electronic Medical Records

As mentioned above, the patient information for a patient stored in thepatient registry may come from a variety of sources. This patientinformation may comprise demographics information, electronic medicalrecords, and lifestyle information, for example. The electronic medicalrecord (EMR) data sources, such as sources 420 in FIG. 4, may comprisevarious sources of electronic medical records including individualdoctor medical practice systems, hospital computing systems, medical labcomputing systems, personal patient devices for monitoring health of thepatient, dietary information, and/or activity information of thepatient, or any other source of medical data that represents aparticular patient's current and historical medical condition. Asdescribed above, the automated systems of the illustrative embodimentsutilize this EMR data from the EMR data sources as a basis forevaluating the historical and/or current medical condition of thepatient to perform various operations, such as generation of apersonalized patient care plan, modifying the personalized patient careplan, and the like. Thus, it is important for these automated systems tobe able to process the information in the EMR data of the patientinformation to determine the information they required to perform theiroperations.

The EMR data itself may comprise clinical values and/or text thatquantify or otherwise represent a degree of a corresponding medicalcondition, or medical value, of the patient being present or not presentin the patient. This quantification may be a simply measure of whethermedical condition is present or not, e.g., the patient tested positiveor negative for a medical condition, or may be a value along a spectrumor range of values. Moreover, each EMR data source may utilize their ownspectrum or range of values and thus, the clinical value may quantifythe medical condition with regard to that EMR data sources ownspectrum/range, but that same clinical value may not translate to thesame quantification/degree in the spectrum/range used by another EMRdata source. The clinical values may take many different forms and mayrepresent any of a plethora of various elements of a medical conditionof the patient. Examples of clinical values include blood pressure,blood glucose levels, protein values, ketones, specific gravity,leukocyte esterase, urobilirubin values, and the like. The particularclinical values are dependent upon the particular medical condition, labtests, medical examination, or the like, and the particularpre-determined system of text or numerical values used by the source ofthe patient information (e.g., EMR data) since not all sourcesnecessarily use the same notations and values to represent similarmedical conditions or medical values.

Because the EMR data may come from a variety of different sources andmay comprise a plethora of different clinical values, sometimes evenbeing defined with regard to the EMR data source's own localspectrum/range of values, it is often difficult to determine what theseclinical values represent. For example, lab results may include thevalue 1.69. However, it is not readily apparent to automated systemswhat this value represents. Similarly, other lab results may usedifferent values to represent the same results depending upon theparticular EMR data source, e.g., one source may use the characterstring “+++” to represent a positive result to a lab test while anothersource may use the character string “pos” to represent a positiveresult, and a third source may utilize a numerical value of “1.0” torepresent a positive result. Thus, without knowing the context in whichthese clinical values are provided, it is not possible for automatedsystems to know what these character strings or numerical values mean.

The illustrative embodiments of the present invention may utilize acognitive natural language processing (NLP) system to analyze thecontext of clinical values in EMR data from a variety of EMR datasources. The cognitive NLP system looks at the surrounding text andother documents in a corpus from the same EMR data source, for example,and/or other EMR data sources, to determine the most likely meaning ofthe clinical value. The cognitive NLP system may operate as part of thePCPCM system 410 in FIG. 4, for example, such as part of the demographicand medical data analysis engine 412, lifestyle data analysis engine413, or the like. Alternatively, the cognitive NLP system may beseparate from these elements and may work in conjunction with theseelements to process the EMR data from the EMR documents received fromvarious EMR and demographics sources 420.

The cognitive NLP system, in response to receiving an EMR document,analyzes the document to extract features from the EMR documentindicative of text, e.g., key terms, key phrases, synonyms, antonyms,medical codes, and other alphanumeric strings of characters that thecognitive NLP system is configured to recognize and which have meaningwithin one or more medical domains. In addition, the cognitive NLPsystem, as part of the feature extraction, extracts numerical valuesthat are potentially clinical values. The extracted features, comprisingthe potential clinical values, are used as a basis for generating one ormore queries that are applied against the received EMR document and/orone or more corpora of medical documentation. The one or more queriesmay comprise combinations of one or more extracted features that aredirected to determining the meaning of a clinical value found in theinput EMR document. The one or more corpora of medical documentation maycomprise medical documentation from the source of the EMR document,and/or medical documentation from a plurality of different EMR documentsources, medical reference documents/texts (such as drug referencedocumentation, medical procedure guides, treatment guides, and thelike), or any other suitable medical documentation that may provideinsight into how to interpret a clinical value.

The queries applied to the one or more corpora generate responses withcorresponding confidence values indicative of what the clinical valuesin the input EMR document 1610 are believed to represent. The confidencevalues may be numerical values calculated based on a frequency of thepresence of the response in the documentation of the one or more corporaand other evidential passages in this documentation that supports theresponse. The confidence values may be weighted based on the associationof the origin of the response with the input EMR document in which theclinical value is present. For example, responses and confidence valuesobtained from the same source as the input EMR document may be givenhigher weighting values than responses and confidence values obtainedfrom other sources. The weighted confidence values are then ranked todetermine a relative ranking of the responses. The ranked responses arethen used as a basis for selecting a most probable meaning of theclinical value.

For example, in response to receiving an input EMR document, a value of169 may be found in the document. This value is extracted as a potentialclinical value. The context of the EMR document is analyzed to identifyother terms, phrases, medical codes, and the like, that are indicativeof the domain of the document, e.g., text may indicate the source of theEMR document as a lab that performs urinalysis tests, a term or phrasein the EMR document may likewise indicate such. For example, there maybe headers in the document that include the phrase “Urinalysis Results,”a medical code may be present in the EMR document that is known to be amedical code for a urinalysis, or the like. These extracted features maybe used as a basis for determining which domain the EMR document belongsto, such as urinalysis, blood test, physical examination, or the like.The domain is essentially a definition of the subject matter that theEMR document covers or comprises. In some illustrative embodiments, thisdomain indicates the medical examination, medical procedure, medicalconsultation, or medical test that the EMR document is documenting. Itshould be appreciated that a single EMR document may, in some cases,cover multiple domains. The domain, or domains, may indicate whichresources to use in order to configure the EMR cognitive NLP system,e.g., which glossaries of terms, which synonyms, antonyms, abbreviationdictionaries, and the like, to use when extracting and interpretingfeatures from the EMR document.

Other terms and phrases, codes, strings, and the like, that arerecognized by the EMR cognitive NLP system may then be extracted usingthe resources with which the EMR cognitive NLP system is configured,e.g., “pro:,” “WBC:”, etc., and used to generate a search query. Thesearch query may search for instances of the extracted features indocuments of the corpus and identify other terms/phrases and values withwhich these instances of the extracted features are associated. Based onthis information, responses are generated with corresponding confidencescores calculated based on a frequency of occurrence of the responses.For example, if the corpus of documentation shows that a large number ofinstances of the term “pro:” appear in the documents in association witha numerical value ranging from 100 to 180, and further is associatedwith the character string “mg/d”, then one response is that the value169 may correspond to the term pro and mg/d, i.e. pro: 169 mg/d and,based on the identified domain, that this is a measure of the “protein”level found in a urinalysis test. This response may have a calculatedconfidence score based on the frequency of the occurrence of thisresponse in the documents of the corpus such that if it is morefrequently found in the corpus, the confidence is greater than responsesthat have a relatively lower frequency of occurrence.

The confidence scores associated with the various responses and/orevidential passages obtained from the corpus are weighted based on thesources of the response/evidential passage. Thus, if the source is thesame or similar to the source of the input EMR document, then theconfidence score is weighted more heavily than confidence scoresassociated with responses/evidence passages from sources that are lesssimilar to that of the source of the input EMR document. Thus, if theresponse of “pro: 169 mg/d” occurs in a document from the same lab, thenthe confidence score is more highly weighted. If a response of “WBC:169” occurs in a blood test document from a different laboratory, thenthe confidence score weighting is relatively lower.

The weighted confidence scores are used to rank the responses accordingto their weighted confidence scores and perform a final confidencemerging and ranking. A final response is selected based on the mergedand ranked listing of responses so as to identify a clinical valuemeaning that is output. In the example above, the meaning for theclinical value of “169” is selected from the ranked listing to be “pro:169 mg/d”. This final selected response is then output to a clinicalvalue guide evaluator that evaluates the clinical value according topre-defined guidance as to the clinical value.

The clinical value guide evaluator may be configured with guidance as toranges/spectrums of clinical values for various clinical measures. Forexample, the patient care plan guideline sources 426 in FIG. 4 mayfurther provide guidelines for various clinical values of clinicalmeasures. These guidelines may be universal across a plurality ofsources or may be specific to a particular source or sub-group ofsources. For example, a guide may indicate that for a urinalysis, aprotein level less than 150 mg/d is normal, a protein level of greaterthan 150 mg/d but less than 170 mg/d is a slightly elevated proteinlevel, and a protein level of greater than 170 mg/d is a highly elevatedprotein level. These ranges may be used to characterize the clinicalvalue found in the input EMR document such that, in the example, theprotein level of 169 is correlated to a slightly elevated protein level.This information may be used with the other patient care plan guidelinesto assist with the generation of a personalized patient care plan and/ormodification of an existing personalized care plan, for example. Otheroperations described above that may utilize a measure of the patient'smedical condition, either historically, currently, or both, may alsoutilize the interpreted meaning of the clinical value.

FIG. 16 is an example block diagram of the primary operational elementsof a clinical value interpretation engine in accordance with oneillustrative embodiment. As shown in FIG. 16, the primary operationalelements comprise an EMR cognitive natural language processing (NLP)system 1600, patient registry 1434, one or more corpora 1645, a clinicalvalue guide evaluator 1690, and an input EMR document update engine1695. The EMR cognitive NLP system 1600 comprises a plurality of stagesof processing, where each stage comprises logic to perform one or moreoperations to facilitate ultimately determining a clinical value'smeaning in an input EMR document. The logic may be provided as softwareexecuted on one or more data processing devices, specially configuredhardware elements, or any combination of executed software and speciallyconfigured hardware elements. For purposes of description, it will beassumed that the logic is provided as software modules executed on oneor more data processing devices, where each module performs theoperations associated with one of the stages of processing in the EMRcognitive NLP system 1600. As noted above, the EMR cognitive NLP system1600 may be part of, or operate in conjunction with, the demographic andmedical data analysis engine 412 in PCPCM system 410, for example.

The EMR cognitive NLP system 1600 receives an input EMR document fromthe patient registry 1434 for processing, such as part of an operationfor generating/modifying a personalized patient care plan, and performsinitial operations for receiving the input EMR document via stage 1610.The input EMR document is then processed as part of a feature extractionstage 1620 to identify a domain of the input EMR document, and therebyidentify resources 1622 to configure the feature extraction logic 1620with resources 1622 corresponding to the domain of the input EMRdocument 1610.

Other terms and phrases, codes, strings, and the like, that arerecognized by the EMR cognitive NLP system 1600 may then be extractedusing the resources 1622 with which the EMR cognitive NLP system isconfigured based on the identification of the domain of the input EMRdocument. In extracting features from the input EMR document, thefeatures are evaluated to identify potential clinical values via theclinical value identification logic stage 1630, along with anycorresponding terms/phrases that are associated with the clinical value.For example, a clinical value may be identified as a numeric valuepresent in the document, a term/phrase that the resources 1622 identifyas indicative of a clinical value, or the like. Thus, numerical valuessuch as 1.69 may be identified as well as terms/phrases comprising textstrings, such as “pos” or “+++” or the like, as clinical values. Textsurrounding or in close proximity to the extracted clinical values mayalso be analyzed and features extracted from these portions of text maybe associated with the corresponding clinical values, e.g., if theclinical value “169” follows the string “pro:” then it may be determinedthat “pro:” may be indicative of the meaning of the value “169” andassociated with this clinical value.

One or more search queries are generated by the query generation logicstage 1640 and applied to a corpus 1647 or corpora 1645 corresponding tothe domain identified during feature extraction 1620. The corpus 1647may comprise a corpus of documents obtained from a same source as theinput EMR document and may be selected from the corpora 1645 based on anidentification of the source of the input EMR document, for example.Alternatively, the corpora 1645 may be used which includes not onlyother documents from the same source as the input EMR document, butdocuments from other similar sources corresponding to the identifieddomain, e.g., other labs performing similar tests, other providers ofmedical services which may include the particular medical service withwhich the input EMR document is associated, or the like.

A search query may search for instances of the extracted featuresassociated with a clinical value, identified in the input EMR document,in documents of the corpus 1647 or corpora 1645 and identify otherterms/phrases and values with which these instances of the extractedfeatures are associated. The responses to the queries are provided backto the hypothesis generation and scoring logic stage 1650 of the EMRcognitive NLP system 1600 for evaluation to generate hypotheses as wellas corresponding confidence scores for these hypotheses. A “hypothesis”in this context is a potential meaning for the clinical value with thevalidity of the hypothesis being measured by the confidence scoreassociated with the hypothesis. Thus, a hypothesis may be that the value“169” is a protein value associated with the text “pro:” since the inputEMR document is a urinalysis document and other documents from the sameor similar sources have used the terms “pro:” and “protein” inassociation with values ranging from 100 to 190 in a similar manner asthe input EMR document.

The responses received from the queries generate are used to generatehypotheses and provide evidential passage support for already generatedhypotheses. Thus, the more frequently occurring correlation ofterms/phrases in the corpus 1647 or corpora 1645 provide a greaterevidential support for a generated hypothesis being a correct one forinterpreting the meaning of the clinical value. In one illustrativeembodiment, this is quantified by a confidence score which is based onthe frequency of occurrence of the correlation of terms/phrases andsimilar clinical values, as well as weighting values applied to theconfidence score based on the sources of the evidential support andoriginal hypothesis.

The weighted confidence scores for the various hypotheses generated instage 1650 are synthesized in stage 1660 such that similar hypothesesare combined to form a single hypothesis with corresponding weightedconfidence score, e.g., a combination of the weighted confidence scoresof the synthesized hypotheses, a greatest confidence score of the two ormore synthesized hypotheses, or the like. The synthesized hypotheses arethen provided to the final confidence merging and ranking logic stage1670 which performs a final merging of the hypotheses and ranking into asingle ranked listing of the hypotheses. This may involve listing thehypotheses in a ranked descending order according to confidence score,for example. This ranked listing is then provided to the clinical valuemeaning selection and output logic stage 1680 where a clinical valuemeaning is selected from the ranked listing of hypotheses and output tothe clinical value guide evaluator 1690 that evaluates the clinicalvalue according to pre-defined guidance as to the clinical value. Itshould be appreciated that the clinical value guide evaluator 1690 is anoptional element and in some embodiments, the clinical value meaning maysimply be output to the other elements of the system performing patientcare operations, e.g., other elements of the PCPCM system 410 in FIG. 4for use in performing their operations for generating/modifyingpersonalized patient care plans.

The clinical value guide evaluator 1690 may be configured with guidanceas to ranges/spectrums of clinical values for various clinical measures.For example, the patient care plan guideline sources 426 in FIG. 4 mayfurther provide guidelines for various clinical values of clinicalmeasures. These guidelines may be universal across a plurality ofsources or may be specific to a particular source or sub-group ofsources. These ranges may be used to characterize the clinical valuefound in the input EMR document into a classification of the clinicalvalue, such as severity category, for example. This information may beused with the other patient care plan guidelines to assist with thegeneration of a personalized patient care plan and/or modification of anexisting personalized care plan, for example. Other operations describedabove that may utilize a measure of the patient's medical condition,either historically, currently, or both, may also utilize theinterpreted meaning of the clinical value.

Alternatively, or in addition, the selected clinical value meaning maybe output to an input EMR document update engine 1695 which compriseslogic for updating the input EMR document to reflect the selectedclinical meaning for the identified clinical value. For example,metadata associated with the input EMR document may be updated toindicate that the particular clinical value in the input EMR document isa urine protein level measure. Moreover, the evaluation of the clinicalvalue generated by the clinical value guide evaluator 1690 may also beprovided to the input EMR document update engine 1695 and used to updatethe input EMR document. For example, the metadata associated with theinput EMR document may be updated to point to the clinical value andassociate the evaluation with the clinical value, e.g., the value 169 isa urine protein level and is considered slightly elevated.

Thus, with the mechanisms of the illustrative embodiments, the clinicalvalues in an input EMR document are automatically processed usingcognitive NLP system logic to determine the meaning behind theparticular clinical value. The mechanisms of the illustrativeembodiments utilize a corpus, or corpora, of documentation from the sameor similar sources of EMR documents to generate hypotheses as to whatthe clinical value represents, or “means”, in the context of the inputEMR document. These hypotheses are evaluated with regard to confidencethat the hypothesis is correct and ranked according to the degree ofconfidence associated with each of the hypotheses. A hypothesis isselected from the ranked listing, e.g., the highest ranked hypothesis isselected, and used as a basis for interpreting the clinical value in theinput EMR document and as a basis for characterizing the clinical valueas well as updating the input EMR document to include informationregarding the selected clinical value meaning and optionally thecharacterization of the clinical value based on the selected clinicalvalue meaning. Moreover, other patient care operations may then beperformed based on the meaning of the clinical value and thecharacterization of the clinical value, e.g., generating/modifyingpersonalized patient care plans.

FIG. 17 is a flowchart outlining an example operation for interpretingclinical values in electronic medical records in accordance with oneillustrative embodiment. As shown in FIG. 17, the operation starts withreceiving an input EMR document, such as from a patient registry (step1710). The receipt of the input EMR document may be, for example, partof an operation for providing a patient care operation to the patient.These patient care operations may comprise any previous operationdirected to providing medical services or care to the patient. Thesepatient care operations may include, for example, generating/modifying apersonalized patient care plan for the patient.

The input EMR document is analyzed and features are extracted todetermine the domain of the input EMR document and then configure theEMR cognitive NLP system to utilize the resources for the identifieddomain when processing the input EMR document (step 1712). As mentionedabove, the patient registry receives documents from a plurality ofdifferent sources and thus, one input EMR document may represent a bloodtest result, another EMR document may represent a urinalysis testresult, still a further input EMR document may be the results of anoffice visit with a physician, or results of an EKG or MRI, or any of aplethora of other types of tests or reports on medical services renderedto the patient. Thus, in order to properly ingest and interpret theinformation present in the input EMR document it is first necessary toidentify the domain of the input document and thereby determine theresources that may be used to further understand the information presentin the input EMR document.

Having identified the domain of the input EMR document and configuredthe EMR cognitive NLP system appropriately, features are extracted fromthe input EMR document (step 1714) and clinical values in theseextracted features, or associated with these extracted features, areidentified along with any associated text features to generatecorrelations of text features and clinical values (step 1716). Searchqueries are then generated based on the correlations of text featuresand clinical values (step 1718) and applied to a corpus or corpora ofdocuments (step 1720). Responses to the search queries are received andcorresponding hypotheses are generated (step 1722). The hypothesesrepresent potential meanings of the clinical value.

Confidence scores are calculated for the various hypotheses (step 1724).These confidence scores may be calculated based on a frequency ofoccurrence of similar correlations of text features and clinical valuesin the documents of the corpus or corpora. These confidence scores maybe weighted according to the similarity of the source with the source ofthe input EMR document, or other suitable characteristics indicative ofthe level of correspondence between the documents in the corpus and theinput EMR document.

The hypotheses are ranked based on the confidence scores (step 1726) anda hypothesis is selected from the ranked listing as the final meaning ofthe clinical value (step 1728). This selected hypothesis is provided toa guideline evaluator which applies guidelines (step 1730) to theclinical value based on the selected hypothesis (or meaning) of theclinical value to thereby characterize the clinical value, such as withregard to severity, for example (step 1732). Based on the selectedmeaning of the clinical value, and/or the characterization of theclinical value based on the selected meaning, the input EMR document maybe updated with metadata indicating the meaning of the clinical valueand/or its characterization. Moreover, in addition, or alternatively,other patient care operations may be performed based on the clinicalvalue's meaning and/or characterization, e.g., generating/modifying apersonalized patient care plan (step 1734). The operation thenterminates.

As noted above, it should be appreciated that the illustrativeembodiments may take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In one example embodiment, the mechanisms of theillustrative embodiments are implemented in software or program code,which includes but is not limited to firmware, resident software,microcode, etc.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers. Network adapters mayalso be coupled to the system to enable the data processing system tobecome coupled to other data processing systems or remote printers orstorage devices through intervening private or public networks. Modems,cable modems and Ethernet cards are just a few of the currentlyavailable types of network adapters.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the describedembodiments. The embodiment was chosen and described in order to bestexplain the principles of the invention, the practical application, andto enable others of ordinary skill in the art to understand theinvention for various embodiments with various modifications as aresuited to the particular use contemplated. The terminology used hereinwas chosen to best explain the principles of the embodiments, thepractical application or technical improvement over technologies foundin the marketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

What is claimed is:
 1. A method, in a data processing system comprisinga processor and a memory, for determining meaning of medical values innatural language content, comprising: receiving, by the data processingsystem, patient information from a source, wherein the patientinformation comprises at least one of text or a numerical valueindicative of a medical value corresponding to a patient; performing, bythe data processing system, cognitive natural language processing on acontext of the text or numerical value in the patient information;determining, by the data processing system, a meaning of the medicalvalue based on results of the cognitive natural language processing; andprocessing, by the data processing system, the patient information fromthe source based on the determined meaning of the medical value.
 2. Themethod of claim 1, wherein a plurality of patient information isreceived from a plurality of different sources, wherein at least twosources in the plurality of different sources utilize differentpre-determined systems of text or numerical values to represent medicalvalues, and wherein the context of the text or numerical value is acontext specific to the particular source that provided the patientinformation.
 3. The method of claim 1, wherein performing cognitivenatural language processing on the context of the text or numericalvalue in the patient information comprises performing analysis based onother documentation in a corpus of documentation comprising at least oneof other patient information from the same or different sources, ormedical reference documents.
 4. The method of claim 3, whereinperforming cognitive natural language processing on the context of thetext or numerical value in the patient information further comprises:extracting features from the patient information including the text ornumerical value; generating one or more queries based on the extractedfeatures; and applying the one or more queries to the corpus ofdocumentation to generate one or more candidate results indicatingcandidate meanings of the medical value, wherein the analysis isperformed on the candidate meanings to select a meaning for the medicalvalue from the candidate meanings.
 5. The method of claim 4, wherein theanalysis comprises evaluating confidence values associated with thecandidate meanings of the medical value and selecting a candidatemeaning as a meaning of the medical value based on the evaluation of theconfidence values.
 6. The method of claim 5, wherein evaluatingconfidence values associated with the candidate meanings of the medicalvalue comprises weighting confidence values associated with candidatemeanings based on sources of documents in the corpus of documentationfrom which the candidate meaning was generated.
 7. The method of claim6, wherein evaluating confidence values comprises weighting confidencevalues associated with candidate meanings that were generated fromdocumentation in the corpus that is from a same source as the source ofthe patient information higher than candidate meanings that weregenerated from documentation in the corpus that is from a differentsource than the source of the patient information.
 8. The method ofclaim 4, wherein extracting features from the patient informationcomprises: extracting features that indicate a domain of the patientinformation, where the domain indicates one of a type of medicalexamination, medical procedure, medical consultation, or medical testthat is documented in the patient information; determining one or moredomains of the patient information based on the extracted features; andconfiguring resources used by the cognitive natural language processingbased on the determined one or more domains.
 9. The method of claim 9,wherein applying the one or more queries to the corpus of documentationto generate one or more candidate results indicating candidate meaningsof the medical value comprises identifying portions of text in documentsof the corpus of documentation that match one or more of the extractedfeatures or features associated with the extracted features asidentified by the resources corresponding to the one or more domains.10. The method of claim 1, wherein processing the patient informationfrom the source based on the determined meaning of the medical valuecomprises: generating a personalized patient care plan based on thedetermined meaning of the medical value; and outputting the personalizedpatient care plan to a computing device associated with the patient. 11.A computer program product comprising a computer readable storage mediumhaving a computer readable program stored therein, wherein the computerreadable program, when executed on a computing device, causes thecomputing device to implement a patient information processing systemwhich operates to: receive patient information from a source, whereinthe patient information comprises at least one of text or a numericalvalue indicative of a medical value corresponding to a patient; performcognitive natural language processing on a context of the text ornumerical value in the patient information; determine a meaning of themedical value based on results of the cognitive natural languageprocessing; and process the patient information from the source based onthe determined meaning of the medical value.
 12. The computer programproduct of claim 11, wherein a plurality of patient information isreceived from a plurality of different sources, wherein at least twosources in the plurality of different sources utilize differentpre-determined systems of text or numerical values to represent medicalvalues, and wherein the context of the text or numerical value is acontext specific to the particular source that provided the patientinformation.
 13. The computer program product of claim 11, whereinperforming cognitive natural language processing on the context of thetext or numerical value in the patient information comprises performinganalysis based on other documentation in a corpus of documentationcomprising at least one of other patient information from the same ordifferent sources, or medical reference documents.
 14. The computerprogram product of claim 13, wherein performing cognitive naturallanguage processing on the context of the text or numerical value in thepatient information further comprises: extracting features from thepatient information including the text or numerical value; generatingone or more queries based on the extracted features; and applying theone or more queries to the corpus of documentation to generate one ormore candidate results indicating candidate meanings of the medicalvalue, wherein the analysis is performed on the candidate meanings toselect a meaning for the medical value from the candidate meanings. 15.The computer program product of claim 14, wherein the analysis comprisesevaluating confidence values associated with the candidate meanings ofthe medical value and selecting a candidate meaning as a meaning of themedical value based on the evaluation of the confidence values.
 16. Thecomputer program product of claim 15, wherein evaluating confidencevalues associated with the candidate meanings of the medical valuecomprises weighting confidence values associated with candidate meaningsbased on sources of documents in the corpus of documentation from whichthe candidate meaning was generated.
 17. The computer program product ofclaim 14, wherein extracting features from the patient informationcomprises: extracting features that indicate a domain of the patientinformation, where the domain indicates one of a type of medicalexamination, medical procedure, medical consultation, or medical testthat is documented in the patient information; determining one or moredomains of the patient information based on the extracted features; andconfiguring resources used by the cognitive natural language processingbased on the determined one or more domains.
 18. The computer programproduct of claim 17, wherein applying the one or more queries to thecorpus of documentation to generate one or more candidate resultsindicating candidate meanings of the medical value comprises identifyingportions of text in documents of the corpus of documentation that matchone or more of the extracted features or features associated with theextracted features as identified by the resources corresponding to theone or more domains.
 19. The computer program product of claim 11,wherein processing the patient information from the source based on thedetermined meaning of the medical value comprises: generating apersonalized patient care plan based on the determined meaning of themedical value; and outputting the personalized patient care plan to acomputing device associated with the patient.
 20. An apparatuscomprising: a processor; and a memory coupled to the processor, whereinthe memory comprises instructions which, when executed by the processor,cause the processor to implement a patient information processing systemwhich operates to: receive patient information from a source, whereinthe patient information comprises at least one of text or a numericalvalue indicative of a medical value corresponding to a patient; performcognitive natural language processing on a context of the text ornumerical value in the patient information; determine a meaning of themedical value based on results of the cognitive natural languageprocessing; and process the patient information from the source based onthe determined meaning of the medical value.